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We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental...
We develop a new method for a particular type of a classification problem, where the positive class is a mixture of multiple clusters and the negative class is drawn from a single cluster. The new method employs an alternating optimization approach, which jointly discovers the clusters in the positive class, and at the same time, optimizes the classifiers that separate each positive cluster from the...
The disadvantages of BOW (Bag of words model) for image classification include the large amount of data in generating a codebook by clustering, redundant code words that may affect the classification results and so on. The process of BOW for the classification can be improved through the Laplace weights to improved fuzzy C means algorithm, and obtaining codebook with more ability to distinguish between...
Investigation into the methods of classification with training for determination of separating surfaces coefficients using optimization methods based on wavelet-transform with and without constraints (in the form of inequalities) is carried out. Recommendations are offered for choice of optimization method for classification in automated medical diagnostics systems with regard to the peculiarities...
Many of the most successful classifiers are based on convex surrogate loss functions. However, it is widely accepted that the 0 – 1 loss would be more natural for classification performance evaluation and many surrogate loss functions can be understood as convex approximations to the 0 – 1 loss. Therefore, in this paper, we attempt to minimize the 0 – 1 loss directly via Mixed Integer Programming...
Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization...
In this paper, we address the problem of multi-instance multi-label learning (MIML) where each example is associated with not only multiple instances but also multiple class labels. In our novel approach, given an MIML example, each instance in the example is only associated with a single label and the label set of the example is the aggregation of all instance labels. Many real-world tasks such as...
The quality of large-scale recommendation systems has been insufficient in terms of the accuracy of prediction. One of the major reasons is caused by the sparsity of the samples, usually represented by vectors of userspsila ratings on a set of items. Combining information other than userspsila ratings can provide the learning model complementary views of the data and, thus, a more accurate prediction...
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