We provide statistical performance guarantees for a recently introduced kernel classifier that optimizes the L2 or integrated squared error (ISE) of a difference of densities. The classifier is similar to a support vector machine (SVM) in that it is the solution of a quadratic program and yields a sparse classifier. Unlike SVMs, however, the L2 kernel classifier does not involve a regularizatio...

The application of machine learning methods to malware detection has opened up possibilities of generating large number of classifiers that use different kinds of features and learning algorithms. A straightforward way to select the best classifier is to pick the one with best holdout or cross-validation performance. Cross-validation or holdout gives a point estimate of generalization performan...

In this paper, an automatic classifier has been developed using Feed Forward Neural Network (FFNN) to classify the ECG signals between different heartbeats. Here, the classifier is trained independently bymorphological, heartbeat interval features and temporal features using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The trained classifier then classifies the be...

A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We descri...

This paper presents a fast and novel method to speed up training and evaluation of support vector machine (SVM) classifiers with a very large set of linear features. A pre-computation step and a redefinition of the kernel function handle linear feature evaluation implicitly and thus result in a run-time complexity as if no linear features were evaluated at all. We then train a classifier for fa...

This paper describes the pattern recognition technique based on multiscale discrete wavelet transform(MDWT) and least square support vector machine (LS-SVM) for the classification of EEG signals. The different statistical features are extracted from each EEG signal corresponding to various seizer and nonsiezer brain functions, using MDWT. Further these sets of features are fed to the LS-SVM mul...

There are several classification problems, which are difficult to solve using a single classifier because of the complexity of the decision boundary. Whereas, a wide variety of multiple classifier systems have been built with the purpose of improving the recognition process. There is no universal method performing the best. The aim of this paper is to show another model of combining classifiers...

In this paper, the performance of a new fuzzy classifier, here called fuzzy β-NN, has been analyzed. The classifier classifies data according to the fuzzy membership values of the reference set inside the prespecified radius β. Members of the reference set outside the radius β have no influence on classification decision. The successful classification by the classifier depends on the parameters...

Using articulatory features for speech recognition improves the performance of low-resource languages. One way to obtain articulatory features is by using an articulatory classifier (pseudoarticulatory features). The performance of the articulatory features depends on the efficacy of this classifier. But, training such a robust classifier for a low-resource language is constrained due to the li...

This paper discusses a new computational scheme based on Functional Networks and applies it to the problem of classification and quantification of gas species in a mixture. A Generalized Functional Network as a new classifier is proposed in order to improve the potentialities of the standard Functional Network classifier. Both methodology and learning algorithm are derived. The performance of t...