@conference {57, title = {Hash Function Learning via Codewords}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2015}, series = {Lecture Notes in Computer Science}, volume = {9284}, year = {2015}, note = {Acceptance rate 23.4\% (89/380).}, pages = {659-674}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data{\textquoteright}s hash codes. Secondly and more importantly, the same framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning tasks in a natural manner. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.}, keywords = {Codeword, Hash function learning, support vector machine}, author = {Yinjie Huang and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {38, title = {Efficient Revised Simplex Method for SVM Training}, journal = {Neural Networks, IEEE Transactions on}, volume = {22}, year = {2011}, month = {Oct.}, pages = {1650-1661}, abstract = {

Existing active set methods reported in the literature for support vector machine (SVM) training must contend with singularities when solving for the search direction. When a singularity is encountered, an infinite descent direction can be carefully chosen that avoids cycling and allows the algorithm to converge. However, the algorithm implementation is likely to be more complex and less computationally efficient than would otherwise be required for an algorithm that does not have to contend with the singularities. We show that the revised simplex method introduced by Rusin provides a guarantee of nonsingularity when solving for the search direction. This method provides for a simpler and more computationally efficient implementation, as it avoids the need to test for rank degeneracies and also the need to modify factorizations or solution methods based upon those rank degeneracies. In our approach, we take advantage of the guarantee of nonsingularity by implementing an efficient method for solving the search direction and show that our algorithm is competitive with SVM-QP and also that it is a particularly effective when the fraction of nonbound support vectors is large. In addition, we show competitive performance of the proposed algorithm against two popular SVM training algorithms, SVMLight and LIBSVM.

}, keywords = {active set method, learning (artificial intelligence), LIBSVM algorithm, quadratic programming, rank degeneracy, set theory, simplex method, support vector machine, Support vector machines, SVM training, SVMLight algorithm}, issn = {1045-9227}, doi = {10.1109/TNN.2011.2165081}, author = {Christopher G. Sentelle and Georgios C. Anagnostopoulos and Michael Georgiopoulos} } @conference {40, title = {Multinomial Squared Direction Cosines Regression}, booktitle = {Neural Networks (IJCNN), The 2011 International Joint Conference on}, year = {2011}, note = {

Acceptance rate 75\% (468/620).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {San Jose, California, USA }, abstract = {

In this paper we introduce Multinomial Squared Direction Cosines Regression as an alternative Multinomial Response Model. The proposed model offers an intuitive geometric interpretation to the task of estimating posterior class probabilities in multi-class problems. In specific, the latter probabilities correspond to the squared direction cosines between a given pattern and representative class exemplars that form a basis in the decision space. We demonstrate that the model allows for efficient training via a trust region based Newton{\textquoteright}s Method, provided that the number of model parameters is not too large. Experimental results on several benchmark classification problems show that it compares competitively against Logistic Regression Classifiers, Support Vector Machines, and Classification and Regression Tree models.

}, keywords = {benchmark classification problem, Computational modeling, decision space, decision theory, geometric interpretation, Kernel, logistic regression classifier, Mathematical model, model parameter, multiclass problem, multinomial response model, multinomial squared direction cosines regression, Newton method, pattern classification, regression analysis, Regression tree analysis, regression tree model, support vector machine, Support vector machines, Training}, doi = {10.1109/IJCNN.2011.6033620}, author = {Naveed H. Iqbal and Georgios C. Anagnostopoulos} }