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In this paper, we propose a novel approach for facial expression analysis and recognition. The main contributions of the paper are as follows. First, we propose an efficient facial expression recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of the AIBO robot. More precisely, the robot is displaying an emotional state in response to the recognized user's facial expression. Experiments using unseen videos demonstrated the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper shows a strategy based on simulated annealing for the optimal tuning of a PID controller to deal with time-varying delay. The main goal is to minimize the integral time absolute error (ITAE) performance index and the overshoot for a drilling-force control system. The proposed strategy is compared with other classic tuning rules (the Ziegler-Nichols and Cohen-Coon tuning formulas). Other tuning laws derived from genetic algorithms and the Simplex search algorithm for unconstrained optimization are also included in the comparative study. The results demonstrate that simulated annealing provides an optimal tuning of the PID controller, which means better transient response (less overshoot) and less ITAE than with other methods. [ABSTRACT FROM AUTHOR]

This paper presents an approach to extracting stock market trading rules from stock market data. Trading rules are based on two multi-layer perceptrons, one generating buy signals and one generating sell signals. Inputs of these perceptrons are fed with values of technical indicators computed on historical stock quotations. Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of trading rules is reasonable and the trading rules are able to generate reasonable trading signals, not only over a training period, used in the training process, but also over a test period, unknown during constructing trading rules. Moreover, trading strategies defined by such trading rules are profitable and often outperform the simple Buy&Hold strategy. [ABSTRACT FROM AUTHOR]

This paper describes an integrated clinical support system combining data entry and access with prognostic modelling, for use by the clinician, complemented by a patient information system tailored to the particular characteristics of the individual patient. The core of the system comprises a modelling methodology based on the PLANN-ARD neural network which combines risk staging with automatic rule generation to derive an explanation facility for the risk group allocation of each patient. The aim of the system is to promote better informed decision making on the part of both the clinician and the patient, exploiting the combined potential of analytical methodologies and the internet. [ABSTRACT FROM AUTHOR]

In this paper the utility of using the Self Organizing Maps (SOM), in conjunction with U-matrix, to visualize the evolution of a social network community formed by a set of blogs is shown. Weblogs are dynamic websites updated via easy-to-use content management systems whose links tend to mirror or in some cases establish new types of social relations, thereby creating a social network. Analyzing the evolution of this network allows the discovery of emerging social structures and their trends in growth. Here we apply this method to Blogalia, a blog hosting site from which we have a complete set of data. The proposed procedure not only gives some insight on how communities form and evolve, but would also enable to predict the future paths that their members will take. [ABSTRACT FROM AUTHOR]

In this paper we explore the interest of computational intelligence tools in the management of heterogeneous communication networks, specifically to predict congestion, failures and other anomalies in the network that may eventually lead to degradation of the quality of offered services. We show two different applications based on neural and neurofuzzy systems for Quality of Service (QoS) management in next generation networks for V2oIP services. The two examples explained in this paper attempt to predict the communication network resources for new incoming calls, and visualizing by means of self-organizing maps the QoS of a communication network. [ABSTRACT FROM AUTHOR]

The topology verification is an important problem in power system engineering. The paper presents the solution for this problem with use of the method, that is independent of state estimation. The method combines utilization of knowledge about power system and radial basis function networks. It allows to perform the power system topology verification as a series of verification processes for particular nodes of a power network. In the paper the possibility of such decomposition is proved. A principle of the method is described. Next, the computational example of topology verification with use of the characterized method is presented. At the end, the features of the method are analyzed, paying special attention to its efficiency. [ABSTRACT FROM AUTHOR]

This paper describes a new platform for FPGA implementation of the multilayer perceptron (MLP) back propagation algorithm (BP). The three implementation figures of the algorithm are considered. These are the off type implementation, the on chip global implementation and the dynamic reconfiguration of the ANN. To achieve our goal, a design for reuse strategy has been applied. To validate our approach, three case studies are considered using the Virtex-II and Virtex-4 FPGA devices. A comparative study is done and new conclusions are given. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. [ABSTRACT FROM AUTHOR]

All neural networks, both natural and artificial, are characterized by two kinds of dynamics. The first one is concerned with what we would call "learning dynamics", in fact the sequential (discrete time) dynamics of the choice of synaptic weights. The second one is the intrinsic dynamics of the neural network viewed as a dynamical system after the weights have been established via learning. The paper deals with the second kind of dynamics. Since the emergent computational capabilities of a recurrent neural network can be achieved provided it has suitable dynamical properties when viewed as a system with several equilibria, the paper deals with those qualitative properties connected to the achievement of such dynamical properties, more precisely the gradient like behavior. In the case of the neural networks with delays, these aspects are reformulated in accordance with the state of the art of the theory of delay dynamical systems. [ABSTRACT FROM AUTHOR]

Multidimensional databases are commonly used for decision making in the context of data warehouses. Considering the multidimensional model, data are presented as hypercubes organized according to several dimensions. However, in general, hypercubes have more than three dimensions and contain a huge amount of data, and so cannot be easily visualized. In this paper, we show that data cubes can be visualized as images by building blocks that contain mostly the same value. Blocks are built up using an APriori-like algorithm and each block is considered as a set of pixels which colors depend on the corresponding value. The key point of our approach is to set how to display a given block according to its corresponding value while taking into account that blocks may overlap. In this paper, we address this issue based on the Pixelization paradigm. [ABSTRACT FROM AUTHOR]

This paper depicts an already developed experience in search for a predictable mechanism with respect to the future performance of a student considering the numerous factors that influence in its failure/success. The use of different neural networks configurations in conjunction with a large data volume on top of detailed attributes consideration for each student makes for an adequate base for the results obtained to be analyzed. The idea behind this paper is to arrange a mechanism that allows us to estimate before hand taking into consideration data from the student in reference to family, social and wealth surroundings for the student future performance identifying those factors that favors the tendency to failure or success. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for behavioural modelling that allows to unravel the key drivers that direct specific cognitive behaviours. In order to do so, a novel framework for clustering based on utility measures is presented that allows to understand the different behaviours that different groups of people may have, and allows the creation of profiles that are relevant with respect to the utility measure. The proposed method is not contrary to other clustering methods but rather builds on the functionality of 'basic' clustering algorithms. A common aim of clustering consists of partitioning a set of patterns into different subsets of patterns which have homogeneous characteristics. In this paper we suggest a more ambitious goal that additionally tries to maximize a utility measure. The paper also describes the results obtained when the method is used to analyze human behaviour in the area of customer intelligence. Specifically, the paper analyzes human behaviour with respect to different socio-demographic and economic indicators and allows to uncover the underlying characteristics that may explain the observed cognitive behaviour. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) approaches are, very often, rendered useless by the statistics of the required sampling process. This paper shows how very fast RL is essentially made possible by abandoning the state feedback during training episodes. The resulting new method, feed-forward learning (FF learning), employs a return estimator for pairs of a state and a feed-forward policy's parameter vector. FF learning is particularly suitable for the learning of controllers, e.g. for robotics applications, and yields learning rates unprecedented in the RL context. This paper introduces the method formally and proves a lower bound on its performance. Practical results are provided from applying FF learning to several scenarios based on the collision avoidance behavior of a mobile robot. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of estimating the following road sign parameters: height, dimensions, visibility distance and partial occlusions. This work belongs to a framework whose main applications involve road sign maintenance, driver assistance, and inventory systems. From this paper we suggest a multisensory system composed from two cameras, a GPS receiver, and a distance measurement device, all of them installed in a car. The process consists of several steps which include road sign detection, recognition and tracking , and road signs parameters estimation. From some trigonometric properties, and a camera model, the information provided by the tracking subsystem and the distance measurement sensors, we estimate the road signs parameters. Results show that the described calculation methodology offers a correct estimation for all types of traffic signs. [ABSTRACT FROM AUTHOR]

In this paper, an implementation of the Region Competition algorithm for segmenting stereoscopic video sequences is shown. This algorithm is an essential task in the method in order to obtain a 3D characterization of artificial muscles. Image sequences are acquired by a two-cam computer vision system. Optimal and efficient segmentation of these images is our goal; information obtained from the segmented first frame of the video sequence is used for segmenting the next frame and so on. Redundancy between stereoscopic pairs of images is also used to optimize the segmentation. In this paper, the Region Competition algorithm is described and our own specific implementation is addressed. Particular problems of stereoscopic video segmentation are shown and how they are solved. Finally, results yielded from simulations are presented and conclusions close the paper. [ABSTRACT FROM AUTHOR]

This paper describes an automatic method for segmenting single and multiple neoplastic hepatic lesions in computed-tomography (CT) images. The structure of the liver is first segmented using the approximate contour model. Then, the appropriate histogram transformations are performed to enhance neoplastic focal lesions in CT images. To segment neoplastic lesions, images are processed using binary morphological filtration operators with the application of a parameterized mean defining the distribution of gray-levels of pixels in the image. Then, the edges of neoplastic lesions situated inside the liver contour are localized. To assess the suitability of the suggested method, experiments have been carried out for two types of tumors: hemangiomas and hepatomas. The experiments were conducted on 60 cases of various patients. Thirty CT images showed single and multiple focal hepatic neoplastic lesions, and the remaining 30 images contained no disease symptoms. Experimental results confirmed that the method is a useful tool supporting image diagnosis of the normal and abnormal liver. The proposed algorithm is 78.3% accurate. [ABSTRACT FROM AUTHOR]

A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterisation. This challenging operation is very important since it is directly linked with the produced optical component's quality. In order to automate this repetitive and difficult task, microscopy based inspection system is aimed. After a defects detection phase, a classification phase is mandatory to complete optical devices diagnosis because a number of correctable defects are usually present beside the potential "abiding" ones. In this paper is proposed a processing sequence, which permits to extract pertinent low-dimensional defects features from raw microscopy issued image. The described approach is validated by studying MLP neural network based classification on real industrial data using obtained defects features. [ABSTRACT FROM AUTHOR]

In this paper we use Kohonen's Self-Organizing Map (SOM) for surveying the financial status of Spanish companies. From it, we infer which are the most relevant variables, so that a fast diagnostic on their status can be reached and, besides, explained via a few rules of thumb extracted from the behavior of those variables. This map can be used as part of a decision making process, or as a first stage in an automatic classification tool. Results show that variables, identified in an easy and visual way (using SOM and U-Matrix graph), are in agreement with those obtained using parametric and non-parametric tests, which are more complex and difficult to apply. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-objective optimization approach to the problem of computing virtual reality spaces for the visual representation of relational structures (e.g. databases), symbolic knowledge and others, in the context of visual data mining and knowledge discovery. Procedures based on evolutionary computation are discussed. In particular, the NSGA-II algorithm is used as a framework for an instance of this methodology; simultaneously minimizing Sammon's error for dissimilarity measures, and mean cross-validation error on a k-nn pattern classifier. The proposed approach is illustrated with an example from cancer genomics data (e.g. lung cancer) by constructing virtual reality spaces resulting from multi-objective optimization. Selected solutions along the Pareto front approximation are used as nonlinearly transformed features for new spaces that compromise similarity structure preservation (from an unsupervised perspective) and class separability (from a supervised pattern recognition perspective), simultaneously. The possibility of spanning a range of solutions between these two important goals, is a benefit for the knowledge discovery and data understanding process. The quality of the set of discovered solutions is superior to the ones obtained separately, from the point of view of visual data mining. [ABSTRACT FROM AUTHOR]

Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based brain-computer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of ANNs as classifiers for a selective attention to auditory stimuli based BCI system. [ABSTRACT FROM AUTHOR]

Evidence-based medicine has grown in stature over the last three decades and is now regarded a key development of modern medicine. The evidence base can be heterogeneous, involving both qualitative knowledge and measured quantitative data. Machine Learning (ML) methods have also begun to establish themselves as an alternative and promising approach to computer-based data analysis in oncology, as this field moves gradually away from being the preserve of traditional statistical analysis. In this paper, we describe the main areas of cancer research in which ML methods are currently being applied, and briefly discuss some of the advantages and disadvantages of their application. [ABSTRACT FROM AUTHOR]

In this paper we describe how the self-organizing map can be applied to the visualization of the evolution and change of Human Immunodeficiency Virus (HIV) quasiespecies, and how this could be converted into a predictive tool. A SOM is trained with a set of nucleic acid sequences from the HIV-1 virus, and the U-Matrix method is applied to discover what natural groups are formed within it. Results show the validity of the method, and allow to discover two groupings within these sets, and what is the evolutionary path taken in them. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new tool of multiple instance learning which is designed using a grammar based genetic programming (GGP) algorithm. We study its application in Web Mining framework to identify web pages interesting for the users. This new tool called GGP-MI algorithm is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) to multiple instance learning. Computational experiments show that, the GGP-MI algorithm obtains competitive results, solves problems of other algorithms, such as sparsity and scalability and adds comprehensibility and clarity in the knowledge discovery process. [ABSTRACT FROM AUTHOR]

Spam, also known as Unsolicited Commercial Email (UCE) is becoming a nightmare for Internet users and providers. Machine learning techniques such as the Support Vector Machines (SVM) have achieved a high accuracy filtering the spam messages. However, a certain amount of legitimate emails are often classified as spam (false positive errors) although this kind of errors are prohibitively expensive. In this paper we address the problem of reducing particularly the false positive errors in anti-spam email filters based on the SVM. To this aim, an ensemble of SVMs that combines multiple dissimilarities is proposed. The experimental results suggest that the new method outperforms classifiers based solely on a single dissimilarity and a widely used combination strategy such as bagging. [ABSTRACT FROM AUTHOR]

Real-life applications of intelligent systems that use neural networks require a high degree of success, usability and reliability. Power systems applications can benefit from such intelligent systems; particularly for voltage stabilization. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. This paper presents an intelligent system which detects voltage instability and classifies voltage output of an assumed power distribution system (PDS) as: stable, unstable or overload. The novelty of our work is the use of voltage output images as the input patterns to the neural network for training and generalizing purposes, thus providing a faster instability detection system that simulates a trained operator controlling and monitoring the 3-phase voltage output of the assumed PDS. Experimental results suggest that our method performs well and provides a fast and efficient system for voltage instability detection. [ABSTRACT FROM AUTHOR]

Morphologically independent vectors correspond to approximations to the vertices of the convex hull covering the data vectors in high dimensional space. We use Morphological Associative Memories (MAM) for the induction of sets of morphologically independent vectors from data. Simultaneous Localization and Mapping (SLAM) is the process of simultaneously building a map of the environment and localizing the mapping agent. In this paper we explore the realization of non-metric SLAM using a visual information based approach relying on morphologically independent images induced from a mobile robot camera image stream. The selected images are proposed as the landmarks for localization, building simultaneously a qualitative map of the environment. We report results of some experiments on data gathered from an indoor ambient. [ABSTRACT FROM AUTHOR]

In this paper we propose a generic approach to acquire navigation skills for nonholonomic vehicles in unknown environments. The algorithm uses reinforcement learning to update both the vehicle model and the optimal behaviour at the same time. After the training phase, the vehicle is able to explore the environment through a wall-following behaviour. The vehicle can also reach any goal position by the virtual wall concept. The method does not require function interpolation to obtain a good approximation to the optimal behaviour. The learning time was only a few minutes to acquire the wall-following behaviour. Both simulation and experimental results are reported to show the satisfactory performance of the method. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to shared control. It consists of combining orders from a mobile and a human. The weight of these orders is obtained by evaluating their corresponding efficiencies but also of his/her condition, which is estimated by continuously monitoring attached biosensors. We rely on a hierarchical architecture where the reactive layer provides a simple and adaptive combination of these sources. We have evaluated the resulting emergent behaviour for different tasks to measure their efficiencies under different circumstances. The system has been successfully tested in real environments from a quantitative and a qualitative point of view. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a typology of career paths in France with the Kohonen algorithm and its generalization to a clustering method of life history using Self Organizing Maps. Several methods have already been proposed to transform qualitative information into quantitative one such as being able to apply clustering algorithm based on the Euclidean distance such as SOM. In the case of life history, these methods generally ignore the longitudinal organization of the data. Our approach aims to deduce a quantitative encode from the labor market situation proximities across time. Using SOM, the topology preservation is also helpful to check when the new encoding keep particularities of the life history and our economic approach of careers. In final, this quantitative encoding can be easily generalized to a method of clustering life history and complete the set of methods generalizing the use of SOM to qualitative data. [ABSTRACT FROM AUTHOR]

Several complex problems have to be solved in order to build Advanced Driving Assistance Systems. Among them, an important problem is the detection and classification of traffic signs, which can appear at any position within a captured image. This paper describes a system that employs independent modules to classify several prohibition road signs. Combining the predictions made by the set of classifiers, a unique final classification is achieved. To reduce the computational complexity and to achieve a real-time system, a previous input feature selection is performed. Experimental evaluation confirms that using this feature selection allows a significant input data reduction without an important loss of output accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an application of neural networks in pattern recognition of defects in sonic signals from non-destructive evaluation by multichannel impact-echo. The problem approached consists in allocating parallelepiped-shape materials in four levels of classifications defining material condition (homogeneous or defective), kind of defects (holes and cracks), defect orientation, and defect dimension. Various signal features as centroid frequency, attenuation and amplitude of the principal frequency are estimated per channel and processed by PCA and feature selection methods to reduce dimensionality. Results for simulations and experiments applying Radial Basis Function, Multilayer Perceptron and Linear Vector Quantization neural networks are presented. Neural networks obtain good performance in classifying several 3D finite element models and specimens of aluminum alloy. [ABSTRACT FROM AUTHOR]

This paper presents configuration methods for an existing neuromorphic hardware and shows first experimental results. The utilized mixed-signal VLSI device implements a highly accelerated network of integrate-and-fire neurons. We present a software framework, which provides the possibility to interface the hardware and explore it from the point of view of neuroscience. It allows to directly compare both spike times and membrane potentials which are emulated by the hardware or are computed by the software simulator NEST, respectively, from within a single software scope. Membrane potential and spike timing dependent plasticity measurements are shown which illustrate the capabilities of the software framework and document the functionality of the chip. [ABSTRACT FROM AUTHOR]

This paper presents an exact reliability analysis of von Neumann multiplexing using majority gates of fan-in Δ = 3, 5, 7, 9, 11, and the corresponding minimum redundancy factors R = 6, 10, 14, 18, 22. Such results are extremely important for a deeper understanding of von Neumann multiplexing (and its variations), especially when considering the expected unreliable behavior of future nano-devices and interconnects. The analysis confirms and augments well-known theoretical results, and is exact as being obtained using exhaustive counting. The extension of the analysis to the device level will allow us to characterize von Neumann multiplexing with respect to device failures for the first time. The results are very timely and are also explaining a strange (non-linear) behavior of von Neuman multiplexing reported two years ago (based on extensive Monte Carlo simulations). [ABSTRACT FROM AUTHOR]

Graphics Processing Units (GPUs) have evolved into powerful programmable processors, faster than Central Processing Units (CPUs) regarding the execution of parallel algorithms. In this paper, an implementation of a Fuzzy ART Neural Network on the GPU is presented. Experimental results show training process is slower on the GPU than on a dual-core Pentium 4 at 3.2 GHz. Once the Neural Network has been trained, the proposed design manages to accelerate Fuzzy ART testing process up to 33 times on a GeForce 7800GT graphics card. [ABSTRACT FROM AUTHOR]

In this paper we present a method for the automatic selection of input variables and some previous parameters, such as number and type of membership functions, in an Adaptive Neuro Fuzzy Inference System (ANFIS) using a Genetic Algorithm with a new fitness function. Both of them constitute a design scheme that we will use for modeling the perception of textures in Digital I-mages. Some examples are presented, training ANFIS with this scheme for mo-deling the following visual textures: coarseness, directionality and regularity. [ABSTRACT FROM AUTHOR]

Network-based applications are essential to providing intelligence to complex electromechanical processes through networked control systems (NCS). The focus of this paper is the design and application of fuzzy logic control for a type of NCSs. In order to assess its feasibility, a networked control system for high-performance milling process, a type of a complex electromechanical process, is implemented on a multi-point interface (MPI) bus, a proprietary programming interface port for peer-to-peer communications that resembles the PROFIBUS protocol. The manipulated input variable the feed rate as well as the control output variable, spindle torque, are transmitted through this network. A simple computational procedure can run remotely as an optimization function without requiring additional hardware. The results demonstrate that the Fuzzy Logic-based strategy provides accuracy, and adequate machining production time thus increasing the metal removal rate. [ABSTRACT FROM AUTHOR]

In this paper a fuzzy system for automatically assessing the students' teamwork performance is presented. The main goal of this work is to guarantee an equitable assessment of students' teamwork throughout the course and across the lecturers of the same subject when subjective criteria are considered. The proposed fuzzy system (i) is designed by using a methodology based on a trade-off between accuracy and intelligibility, and (ii) uses as input linguistic variables a set of four statistical-based parameters, computed from real individual and group marks, which have been subjectively and objectively validated. Finally, the fuzzy system is described and validated experimentally. [ABSTRACT FROM AUTHOR]

In this paper we present application of various versions of the particle swarm optimization method (PSO) in the process of generation of multiple-classifier systems (MCS). While some of the investigated optimisation problems naturally lend themselves to the type of optimisation for which PSO is most suitable we present some other applications requiring non-standard representation of the particles as well as handling of constraints in the optimisation process. In the most typical optimisation case the continuous version of PSO has been successfully applied for the optimization of a soft-linear combiner. On the other hand, one of the adapted binary versions of PSO has been shown to work well in the case of multi-stage organization of majority voting (MOMV), where the search dimension is high and the local search techniques can often get stuck in local optima. All three presented PSO based methods have been tested and compared to each other and to forward search and stochastic hillclimber for five real-world non-trivial datasets. [ABSTRACT FROM AUTHOR]

This paper presents a new cooperative-coevolutive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm promotes a coevolutive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. As credit assignment three quality factors are considered which measure the role of the RBFs in the whole RBFN. In order to calculate the application probability of the coevolutive operators a Fuzzy Rule Base System has been used. The algorithm evaluation with different datasets has shown promising results. [ABSTRACT FROM AUTHOR]

Macroevolutionary Algorithms seem to work better than other Evolutionary Algorithms in problems characterized by having small populations where the evaluation of the individuals is computationally very expensive or is characterized by a very difficult search space with multiple narrow hyper-dimensional peaks and large areas between those peaks showing the same fitness value. This paper focuses on some aspects of Macroevolutionary Algorithms introducing some modifications that address weak points in the original algorithm, which are very relevant in some types of complex real world problems. All the modifications on the algorithm are tested in real world problems. [ABSTRACT FROM AUTHOR]

An important issue in data-mining is to find effective and optimal forms to learn and preserve the topological relations of highly dimensional input spaces and project the data to lower dimensions for visualization purposes. In this paper we propose a novel ensemble method to combine a finite number of Self Organizing Maps, we called this model Fusion-SOM. In the fusion process the nodes with similar Voronoi polygons are merged in one fused node and the neighborhood relation is given by links that measures the similarity between these fused nodes. The aim of combining the SOM is to improve the quality and robustness of the topological representation of the single model. Computational experiments show that the Fusion-SOM model effectively preserves the topology of the input space and improves the representation of the single SOM. We report the performance results using synthetic and real datasets, the latter obtained from a benchmark site. [ABSTRACT FROM AUTHOR]

This paper proposes to apply the branch and bound principle from combinatorial optimization to the Dissimilarity Self-Organizing Map (DSOM), a variant of the SOM that can handle dissimilarity data. A new reference model optimization method is derived from this principle. Its results are strictly identical to those of the original DSOM algorithm by Kohonen and Somervuo, while its running time is reduced by a factor up to 2.5 compared to the one of the previously proposed optimized implementation. [ABSTRACT FROM AUTHOR]

A quite challenging research field in the artificial intelligence domain is the design and evaluation of agents handling automated negotiations on behalf of their human or corporate owners. This paper aims to enhance such agents with techniques enabling them to predict their opponents' negotiation behaviour and thus achieve more profitable results and better resource utilization. The proposed learning techniques are based on MLP and GR neural networks (NNs) that are used mainly to detect at an early stage the cases where agreements are not achievable, supporting the decision of the agents to withdraw or not from the specific negotiation thread. The designed NN-assisted negotiation strategies have been evaluated via extensive experiments and are proven to be very useful. [ABSTRACT FROM AUTHOR]

This paper presents a study in which a new technique for automatically developing Artificial Neural Networks (ANNs) by means of Evolutionary Computation (EC) tools is compared with the traditional evolutionary techniques used for ANN development. The technique used here is based on network encoding on graphs and also their performance and evolution. For this comparison, 2 different real-world problems have been solved using various tools, and the results are presented here. According to them, the results obtained with this technique can beat those obtained with other ANN development tools. [ABSTRACT FROM AUTHOR]

This paper deals with power-quality (PQ) event detection, classification and characterization using higher-order sliding cumulants to examine the signals. Their maxima and minima are the main features, and the classification strategy is based in competitive layers. Concretely, we concentrate on the task of differentiating two types of transients (short duration and long duration). By measuring the fourth-order central cumulants' maxima and minima, we build the two-dimensional feature measured vector. Cumulants are calculated over high-pass digitally filtered signals, to avoid the low-frequency 50-Hz signal. We have observed that the minima and maxima measurements produce clusters in the feature space for 4th-order cumulants; third-order cumulants are not capable of differentiate these two very similar PQ events. The experience aims to set the foundations of an automatic procedure for PQ event detection. [ABSTRACT FROM AUTHOR]

In this paper, the behavior of some hebbian artificial neural networks with lateral weights is analyzed. Hebbian neural networks are employed in communications and signal processing applications for implementing on-line Principal Component Analysis (PCA). Different improvements over the original Oja model have been developed in the last two decades. Among them, models with lateral weights have been designed to directly provide the eigenvectors of the correlation matrix [1,5,6,9]. The behavior of hebbian models has been traditionally studied by resorting to an associated continuous-time formulation under some questionable assumptions which are not guaranteed in real implementations. In this paper we employ the alternative deterministic discrete-time (DDT) formulation that characterizes the average evolution of these nets and gathers the influence of the learning gains time evolution [12]. The dynamic behavior of some of these hebbian models is analytically characterized in this context and several simulations complement this comparative study. [ABSTRACT FROM AUTHOR]

The performance of the Naïve Bayes classifier can be improved by appropriate preprocessing procedures. This paper presents a comparative study of three preprocessing procedures, namely Principle Component Analysis (PCA), Independent Component Analysis (ICA) and class-conditional ICA, for Naïve Bayes classifier. It is found that all the three procedures keep improving the performance of the Naïve Bayes classifier with the increase of the number of attributes. Although class-conditional ICA has been found to be superior to PCA and ICA in most cases, it may not be suitable for the case where the sample size for each class is not large enough. [ABSTRACT FROM AUTHOR]

We propose in this paper a visualization approach for large online databases using the Hilbert space-filling curve to map N-dimensional data points to 2D or 3D points. Dimensionality reduction methods like principal component analysis (PCA), multi dimensional scaling (MDS) or self organizing maps (SOMS) can map N-dimensional data points with N>>3 into 3 dimensional or 2 dimensional values that allow us to visualize the data. These methods although popular, require either the calculation of a scatter matrix, eigenvalues and eigenvectors, or the iteration of learning algorithms. Therefore these methods cannot perform online, can be slow with large databases and always produce information loss when the data is mapped from the multidimensional space to the 2D or 3D image. Space-filling curves like the Peano, Z, and Hilbert curve, on the contrary, produce a 1-to-1 mapping between points in a line segment and an arbitrary N-Dimensional hypercube. This 1-to-1 mapping guarantees that there is no information loss on the transformation. Specifically the Hilbert space-filling curve is known to preserve the Lebesgue measure and has been proven to produce an optimal mapping in the sense that an arbitrary contiguous block of information will receive the minimum number of splits in the mapped space. The Hilbert space-filling curve has been extensively used for indexing and clustering by mapping N-dimensional data points to 1-dimensional values. We propose here to use the curve to map to 2 or 3 dimensions for purposes of visualization: By taking advantage of its 1-to-1 nature, a new and generic method to map N-dimensional data points to 2D or 3D points using the Hilbert space-filling curve is developed. We prove theoretically that the calculation of the mapping can be done in constant time if we fix the order of approximation, thereby giving linear O(n) performance on the number of data points to map. We create a Hilbert space-filling curve visualization tool that is much faster than the other methods mentioned and allows us to generate quickly for very large datasets various different visualizations of the data, thereby compensating the lack of use of statistical information in the calculation of the mapped points. We compare our approach to MDS and PCA with a benchmark data set and three real datasets using the distance preserving and topology preserving measure as benchmarks. Our experiments indicate that the Hilbert space-filling curve produces acceptable quality of mapping while achieving much faster visualization and is therefore especially useful for online visualization of very large data sets. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to graphically represent the tracking data in Course Management Systems in order to mine and discover the usage of specific software modules. We have implemented a tool that aims to give, at a glance, a visual representation of the usage of a specific module in all the courses managed by the Course Management System. The tool also allows comparing the usage in different courses, and seeing the distribution of the usage over time. This information is useful to administrators of the Course Management System that have to know how much the modules have been used in courses. But also, it could be useful to instructional designers that have to design, plan, and evaluate the learning needs in institutions. [ABSTRACT FROM AUTHOR]