In this paper, we propose to use support vector machines for classification of bacterial growth and non growth database and modeling the probability of growth. Unlike artificial neural networks paradigms, support vector machines use the kernel functions and support vectors with maximum margin, which allows a better performance. As a practical application of the new approach, support vector mach...

Support vector machines, let them be bi-class or multi-class, have proved efficient for protein secondary structure prediction. They can be used either as sequence-to-structure classifier, structure-to-structure classifier, or both. Compared to the classifier most commonly found in the main prediction methods, the multi-layer perceptron, they exhibit one single drawback: their outputs are not c...

We show that the multi-class support vector machine (MSVM) proposed by Lee et al. (2004) can be viewed as a MAP estimation procedure under an appropriate probabilistic interpretation of the classifier. We also show that this interpretation can be extended to a hierarchical Bayesian architecture and to a fully-Bayesian inference procedure for multiclass classification based on data augmentation....

Support vector machines (SVMs) play a very dominant role in data classification due to their good generalization performance. However, they suffer from the high computational complexity in the classification phase when there are a considerable number of support vectors (SVs). Then it is desirable to design efficient algorithms in the classification phase to deal with the datasets of realtime pa...

This paper discusses about combining Support Vector Machine and decision trees for multi class text classification. Support Vector Machines are trained on each class at each level of the tree and the SVM which is more successful in predicting a class at that level is selected as the decision in that node. Thus a tree is constructed with different SVM in each node. And the tree constructed is us...

In this paper, we derive a general formulation of support vector machines for classification and regression respectively. Le loss function is proposed as a patch of L1 and L2 soft margin loss functions for classifier, while soft insensitive loss function is introduced as the generalization of popular loss functions for regression. The introduction of the two loss functions results in a general ...

LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets.

Text mining is variance of a field called data mining. To make unstructured data workable by the computer Text mining is used which is also referred as “Text Analytics”. Text categorization, also called as topic spotting is the task of automatically classifies a set of documents into groups from a predefined set. Text classification is an essential application and research topic because of incr...

This thesis presents an investigation of the Collection of Parts Model for object categorization. Multiclass categorization is performed using the Collections of Parts model. Results using Support Vector Machines, L1 Logistic Regression and Boosted Decision Trees are presented and discussed. Methods to analyze confusion in these results are developed and results are presented. The Collections o...

This paper extends binary support vector machines to multiclass classification for recognising emotions from speech. We apply two standard schemes (one-versus-one and one-versusrest) and two schemes that form a hierarchy of classifiers each making a distinct binary decision about class membership, on three publicly-available databases. Using the OpenEAR toolkit to extract more than 6000 feature...