Search

Online multiclass classification is a specific problem of online learning that performs a sequence of multiclass classification tasks given the knowledge of previous tasks. The goal is to make correct predictions for this sequence. It is generally considered a more complicated problem than its binary counterpart, online binary classification. A popular algorithm, called the passive-aggressive algorithm, was primarily proposed for binary problems and later extended as the multiclass passive-aggressive (MPA) algorithm for multiclass problems. The nature of MPA allows itself to implement the kernel trick, which enables us to make better predictions with a kernel-based model. However, this approach suffers from the curse of kernelization that causes unbounded growth of the model in memory usage and runtime. To solve the growth problem, we first introduce a resource perspective that gives an alternative and equivalent interpretation of the kernel-based MPA algorithm. Based on the resource perspective, we propose the budgeted MPA (BMPA) algorithm, which approximates the kernel-based MPA algorithm. BMPA limits the maximum number of available resources by removal and fully exploits them through a constrained optimization. We study three removal strategies and give a relative mistake bound that provides a unified analysis. Simulation experiments on various datasets are conducted to demonstrate that BMPA is effective and competitive with state-of-the-art budgeted online algorithms.

Based on an online-learning algorithm, we usually can create a dynamic prediction model and transform a linear model into a non-linear one using kernel functions. This study derives a new online-learning algorithm by combining the popular online-learning algorithm, Passive-Aggressive algorithm, with kernels and the concept of budget. The new algorithm creates a non-linear model and manages the resources used by the model. The proposed algorithm is tested on the network traffic data and shows its potentials for IoT applications.

We consider a general framework for constructing non-linear generators by adding a (32-bit or larger) pseudo-random number generator (PRNG) as a baseline generator to the basic RC4 design, in which an index-selection scheme similar to RC4 is used. We refer to the proposed design as the eRC (enhanced/extended RC4) design. We discuss several advantages of adding a good baseline generator to the RC4 design, including new updating schemes for the auxiliary table. We consider some popular PRNGs with the nice properties of high-dimensional equi-distribution, efficiency, long period, and portability as the baseline generator. We demonstrate that eRC generators are very efficient via extensive empirical testing on some eRC generators. We also show that eRC is flexible enough to choose minimal design parameters for eRC generators and yet the resulting eRC generators still pass stringent empirical tests, which makes them suitable for both software and hardware implementations.

Intermittent demand data is one of the data types with a very random pattern, for example demand data. The data will have value (not zero) if there is a demand. If there is no demand, the data is zero. Intermittent demand data is usually called customer demand data or sales data for an item that is not sold every time. The general problem is that demand is not always continuous but intermittent. This natural fact makes intermittent data not easy to predict. Standard methods used to predict intermittent demand data are Croston. Single exponential smoothing (SES) is also commonly used in practice. The Croston and exponential smoothing generally produce static forecasts. This study proposes a deep learning method i.e. recurrent neural network (RNN) and deep neural network (DNN) to predict intermittent data. The simulation study was carried out by generating data with 72 different design parameters and doing 50 repetitions. Also, the empirical study uses M5 competition data from the Kaggle website. This study aims to measure the performance of RNN and DNN, compared to Croston and SES as the benchmark methods, in predicting intermittent demand data. The performance measurement uses mean absolute error (MAE) and root mean squared scaled error (RMSSE). Deep learning performance in simulation studies successfully outperformed Croston and SES in several design parameters. In empirical studies, only the RNN method outperformed the benchmark methods. This study also found other information that the measurement of MAE is more robust than RMSSE.

Deep learning development nowadays has attracted a lot of attention because of its effectiveness and good performance. The performance of deep learning in medical images analysis already can compete with medical image experts. However, there are experts that still believe deep learning only efficient for the big datasets, because of deep learning performance in small datasets still not satisfying enough. In this study, it is aimed to build a deep learning model for image classification that can achieve high accuracy using chest X-ray images with a relatively small dataset. We classify chest X-ray into a binary classification which is a normal image and image with abnormalities. We built and experimented our model using the public dataset of Shenzen Hospital dataset. We also use a different type of input based on different images preprocessing so that the model can perform accurate classification. Based on the result, pre-trained CheXNet with a newly trained fully connected network on the cropped dataset can achieve the accuracy 0.8761, the sensitivity 0.8909, and the specificity 0.8621. The performance of the model also influenced by the certain region inside the images, such as other regions outside the lung region and black colored region outside the body region.

This paper presents a budgetary learning algorithm for online multiclass classification. Based on the multiclass passive-aggressive learning with kernels, we introduce a dual perspective that gives rise to the proposed budgetary algorithm. Basically, the proposed algorithm limits the amount of data in use and fully exploits the available data on hand through optimization. The algorithm has both constant time and space complexities and thus can avoid the curse of kernelization. Experimental results with open datasets show that the proposed budgetary algorithm is competitive with state-of-the-art algorithms.

This brief proposes a class of nonlinear pseudorandom number generators (PRNGs) based on coupling linear generators (DX generators) with a nonlinear generator (digitized Logistic map). By breaking the linearity, the unpredictability of the proposed generators in the family of coupling PRNGs is enhanced while the nice properties of efficiency, long period, and excellent statistical properties are preserved. Furthermore, the hardware efficiencies are validated by hardware implementations using a TSMC 0.18μm CMOS process with a throughput rate greater than 8,000 Mbit/s. In addition to the extremely long period length, the proposed nonlinear PRNGs can generate random sequences that fulfill all the randomness requirements of NIST SP 800-22 test suite.

Spectral matting is a useful technique for image matting problem. A crucial issue of spectral matting is to determine the number of matting components which has large impacts on the matting performance. In this paper, we propose an improved framework based on spectral matting in order to solve this limitation. Iterative K-means clustering with the assistance of the modularity measure is adopted to obtain the hard segmentation that can be used as the initial guess of soft matting components. The number of matting components can be determined automatically because the improved framework will search possible image components by iteratively dividing image subgraphs.

The state of art of positron emission tomography (PET) takes into account the accidental coincidence events and attenuation. The maximum likelihood estimator can handle this kind of random variation in the reconstruction of a PET image. However, the reconstruction is ill-posed and needs regularization. The boundary information, either from an expert or from the other medical modality of the same object, like the X-ray CT scan, MRT, and so forth, can be used to regularize the reconstruction. The authors have investigated new, efficient and robust approaches to extract the related but incomplete boundary information. Fast algorithms adapted from the expectation/conditional maximization (ECM) and space alternating generalized expectation maximization (SAGE) algorithms are proposed to accelerate the computation. The method of generalized approximate cross validation (GACV) is adjusted to select the penalty parameter from observed data quickly. The Monte Carlo studies demonstrate the improvement.