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The dissimilarity representation for designing pattern recognition systems is analyzed for its ability to build new knowledge from examples using an anti-essentialist approach. It is argued that it may find universals (pattern classifiers) from particulars (training set of examples) but that the resulting knowledge can just be applied but not accessed. Consequently, its use for a conscious human decision maker is limited. [ABSTRACT FROM AUTHOR]

Abstract: In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the assumption that instances are drawn from a mixture distribution of the concept and the non-concept, which leads to a convenient way to solve MIL as a classifier combining problem. It is shown that instances can be classified with any standard supervised classifier by re-weighting the classification posteriors. Given the instance labels, the label of a bag can be obtained as a classifier combining problem. An optimal decision rule is derived that determines the threshold on the fraction of instances in a bag that is assigned to the concept class. We provide estimators for the two parameters in the model. The method is tested on a toy data set and various benchmark data sets, and shown to provide results comparable to state-of-the-art MIL methods. [Copyright &y& Elsevier]

Abstract: This study presents a system for detecting and scoring of a knee disorder, namely, osteoarthritis (OA). Data used for training and recognition is mainly data obtained through computerized gait analysis, which is a numerical representation of the mechanical measurements of human walking patterns. History and clinical characteristics of the subjects such as age, body mass index and pain level are also included in decision-making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren–Lawrence scale: “Normal”, “Mild”, “Moderate”, and “Severe”. Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e. implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. The approach is similar to the Mixture of Experts method. Individual feature selection is applied using the Mahalanobis distance measure and most discriminatory features are used for each expert MLP. The system is tested by a separate set and a success rate of about 80% is achieved on the average. [Copyright &y& Elsevier]