We investigate a new kernel–based classifier: the Kernel Fisher Discriminant (KFD). A mathematical programming formulation based on the observation that KFD maximizes the average margin permits an interesting modification of the original KFD algorithm yielding the sparse KFD. We find that both, KFD and the proposed sparse KFD, can be understood in an unifying probabilistic context. Furthermore,...

Future technologies such as Brain-Computer Interaction Technologies (BCIT) or affective Brain Computer Interfaces (aBCI) will need to function in an environment with higher noise and complexity than seen in traditional laboratory settings, and while individuals perform concurrent tasks. In this paper, we describe preliminary results from an experiment in a complex virtual environment. For analy...

The prominent modeling technique for speech recognition today is the hidden Markov model with Gaussian emission densities. They have suffered, though, from an inability to learn discriminative information and are prone to overfitting and overparameterization. Recent work on machine learning has moved toward models such as the support vector machine that automatically control generalization and ...

Problem statement: The objective of this study is, to find the smallest set of genes that can ensure highly accurate classification of cancer from micro array data by using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: The computational burden and noise arising from irrelevant genes are much reduced; the cost for cancer testing is reduced ...

Relevance Feedback is an interesting procedure to improve the performance of Content-Based Image Retrieval systems even when using low-level features alone. In this work we compare the efficiency of one class and two class Support Vector Machines in content-based image retrieval using Invariant Feature Histograms. We describe our methodology of performing Relevance Feedback in both cases and re...

The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions data points. Recently, multilevel approaches to train SVMs have been developed allow for time-efficient datasets. While regular perform the entire in one—time-consuming—optimization step, first build a hierarchy problems decreasing size that resemble original problem and then an SVM model eac...