In this paper, we study the human action classification problem based on motion features directly extracted from video. In order to implement a fast classification system, we select simple features that can be obtained from non-intensive computation. We also introduce the new SVM 2K classifier that can achieve improved performance over a standard SVM by combining two types of motion feature vec...

Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented. They are shown to give very good results and to be computationally more effective than the branch and bound method.

Bankruptcy prediction is an extensively researched topic. Also ensemble methodology has been applied to it. However, the interpretability of the results, so often important in practical applications, has not been emphasized. This paper builds ensembles of locally linear models using a forward variable selection technique. The method applied to four datasets provides information about the import...

We show that the problem of minimizing the sum of arbitrary-norm real distances to misclassified points, from a pair of parallel bounding planes of a classification problem, divided by the margin (distance) between the two bounding planes, leads to a simple parameterless linear program. This constitutes a linear support vector machine (SVM) that simultaneously minimizes empirical error of miscl...

The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain scenarios where the lasso is inconsistent for variable selection. We then propose a new version of ...

This paper describes a machine learning method, called Regression by Selecthtg Best P~’ttllll’es (RSBF). RSBF consists of two phases: The first phase aims to find the predictive power of each feature by constructing simple linear regression lines, one per each continuous feature and number of categories pen each categorical feature. Although the predictive power of a continuous feature is const...