We address the problems of sparse multiclass and multilabel classifier design and devise new algorithms using margin based ideas. Many online applications such as image classification or text categorization demand fast inference. State-of-the-art classifiers such as Support Vector Machines (SVM) are not preferred in such applications because of slow inference, which is mainly due to the large n...

Support Vector Machines (SVM) has been shown to be a powerful nonparametric classification technique even for high-dimensional data. Although predictive ability is important, obtaining an easy-to-interpret classifier is also crucial in many applications. Linear SVM provides a classifier based on a linear score. In the case of functional data, the coefficient function that defines such linear sc...

Pedestrians are the vulnerable participants in transportation system when crashes happen. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems (ITSs) and safety driving assistant systems (SDASs). This paper proposes a two-stage pedestrian detection method based on machine vision. In the first stage, AdaB...

Spectral band selection is a fundamental problem in hyperspectral classification. This paper addresses the problem of band selection for hyperspectral remote sensing image and SVM parameter optimization. First, we propose an evolutionary classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have opt...

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary ...

In this paper, a novel method for web page hierarchical classification is addressed. In our approach, SVM is used as the basic algorithm to separate any two sub-categories under the same parent node. In order to alleviate the ill shift of SVM classifier caused by imbalanced training data, we try to combine the original SVM classifier with BEV algorithm to create classifier called VOTEM. Then, a...

A novel technique is proposed for improving the standard Vapnik-Chervonenkis (VC) dimension estimate for the Support Vector Machine (SVM) framework. The improved VC estimates are based on geometric arguments. By considering bounding ellipsoids instead of the usual bounding hyperspheres and assuming gap-tolerant classifiers, a linear classifier with a given margin is shown to shatter fewer point...

This paper presents a least square support vector machine (LS-SVM) that performs text classification of noisy document titles according to different predetermined categories. The system’s potential is demonstrated with a corpus of 91,229 words from University of Denver’s Penrose Library catalogue. The classification accuracy of the proposed LS-SVM based system is found to be over 99.9%. The fin...

There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature of their functional commonality and sequence homology. SVM is used to search the undiscovered peptide sequence space and identify Pareto-optimal can...

Human action recognition has a wide range of promising applications like video surveillance, intelligent interface, and video retrieval. The objective of this project is to recognize and annotate different human activities present in digital videos taken from both constrained and unconstrained environment. This work extended the use of the techniques existing in object recognition in 2D images ...