In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from SVMs. Finally, we question both trend...

In this work, we developed classifiers to distinguish between four ovarian tumor types using Bayesian least squares support vector machines (LS-SVMs) and kernel logistic regression. Input selection using rank-one updates for LS-SVMs performed better than automatic relevance determination. Evaluation on an independent test set showed good performance of the classifiers to distinguish between all...

We explore how virtual examples (artificially created examples) improve performance of text classification with Support Vector Machines (SVMs). We propose techniques to create virtual examples for text classification based on the assumption that the category of a document is unchanged even if a small number of words are added or deleted. We evaluate the proposed methods by Reuters-21758 test se...

Support vector machines (SVMs) are trained to detect acoustic-phonetic landmarks, and to identify both the manner and place of articulation of the phones producing each landmark with high accuracy. The discriminant outputs of these SVMs are used as input features for a standard HMM based ASR system. There is a significant improvement in both the phone and word recognition accuracy when using th...

This paper investigates a probabilistic speaker identification method based on the dual Penalized Logistic Regression Machines (dPLRMs). The machines employ kernel functions which map an acoustic feature space to a higher dimensional space as is the case with the Support Vector Machines (SVMs). Nonlinearity in discriminating each speaker is implicitly handled in this space. While SVMs maximize ...

We describe the experiments of the two learning algorithms for Named Entity Recognition. One implements Conditional Random Fields (CRFs), another makes use of Support Vector Machines (SVMs). Both are trained with a large number of features. While SVMs employ purely input features, CRFs also exploit statistical aspects in terms of unigram and bigram of both features and output tags. The main cha...

Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated.

According to statistical learning theory, we propose a feature selection method using support vector machines (SVMs). By exploiting the power of SVMs, we integrate the two tasks, feature selection and classifier training, into a single consistent framework and make the feature selection process more effective. Our experiments show that our SVM feature selection method can speed up the classific...

In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are realizations of an i.i.d. process. In this paper we go far beyond this classical framework by showing that support vector machines (SVMs) essentially only require that the data-generating process satisfies a certain law of large numbers. We then consider the learnability of ...

the aim of this work is to examine the feasibilities of the support vector machines (svms) and k-nearest neighbor (k-nn) classifier methods for the classification of an aquifer in the khuzestan province, iran. for this purpose, 17 groundwater quality variables including ec, tds, turbidity, ph, total hardness, ca, mg, total alkalinity, sulfate, nitrate, nitrite, fluoride, phosphate, fe, mn, cu, ...