Binary classification by minimizing the mean squared slack

The paper presents a new binary classification method based on the minimization of the slack variables energy called the Mean Squared Slack (MSS). We deliver preliminary mathematical results which support the motivation behind our approach. We show that (a) in the linearly separable case the minimum MSS is attained at a separating vector, while (b) the minimizer in the linearly non-separable case is bounded but not zero. The method is conceptually simple: it solves a linear system at each iteration and it converges, typically, within a few iterations. Its complexity is obviously related to the size of the system which, in the linear case, is equal to the input pattern dimension. The method is extended to the non-linear case using kernels. Simulations demonstrate that the method is competitive with respect to computation time, accuracy, and generalization performance compared to state of the art SVM methods. © 2012 IEEE

Towards minimizing the energy of slack variables for binary classification

This paper presents a binary classification algorithm that is based on the minimization of the energy of slack variables, called the Mean Squared Slack (MSS). A novel kernel extension is proposed which includes the withholding of just a subset of input patterns that are misclassified during training. The later leads to a time and memory efficient system that converges in a few iterations. Two datasets are exploited for performance evaluation, namely the adult and the vertebral column dataset. Experimental results demonstrate the effectiveness of the proposed algorithm with respect to computation time and scalability. Accuracy is also high. In specific, it equals 84.951% for the adult dataset and 91.935%, for the vertebral column dataset, outperforming state-of-the-art methods. © 2012 EURASIP

A Systematic Comparison of Music Similarity Adaptation Approaches

In order to support individual user perspectives and different retrieval tasks, music similarity can no longer be considered as a static element of Music Information Retrieval (MIR) systems. Various approaches have been proposed recently that allow dynamic adaptation of music similarity measures. This paper provides a systematic comparison of algorithms for metric learning and higher-level facet distance weighting on the MagnaTagATune dataset. A crossvalidation variant taking into account clip availability is presented. Applied on user generated similarity data, its effect on adaptation performance is analyzed. Special attention is paid to the amount of training data necessary for making similarity predictions on unknown data, the number of model parameters and the amount of information available about the music itself. 1

Ridge-Adjusted Slack Variable Optimization for Supervised Classification

This paper presents an iterative classification algorithm called Ridge-adjusted Slack Variable Optimization (RiSVO). RiSVO is an iterative procedure with two steps: (1) A working subset of the training data is selected so as to reject "extreme" patterns. (2) the decision vector and threshold value are obtained by minimizing the energy function associated with the slack variables. From a computational perspective, we have established a sufficient condition for the "inclusion property" among successive working sets, which allows us to save computation time. Most importantly, under the inclusion property, the monotonic reduction of the energy function can be assured in both substeps at each iteration, thus assuring the convergence of the algorithm. Moreover, ridge regularization is incorporated to improve the robustness and better cope with over-fitting and ill-conditioned problems. To verify the proposed algorithm, we conducted simulations on three data sets from the UCI database: adult, shuttle and bank. Our simulation shows stability and convergence of the RiSVO method. The results also show improvement of performance over the SVM classifier

Profiling Users by Modeling Web Transactions

Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques to profile a user. We assess the efficacy and speed of our method at differentiating 25 users on a dataset representing 6 months of web traffic monitoring from a small company network.Comment: Extended technical report of an IEEE ICDCS 2017 publicatio