In this paper, we propose a dynamic approach to feature and classifier selection. In our approach, based on performance, visual features and classifiers are selected automatically. In earlier work, we presented the Visual Apprentice, in which users can define visual object models via a multiple-level object definition hierarchy (region, perceptual-area, object-part, and object). Visual Object D...

A new method of text-dependent speaker identification using discriminative feature selection is proposed in this paper. The characteristics of the proposed method are as follows: feature parameters extraction, vector quantization with the growing neural gas (GNG) algorithm, model building using gaussian distributions and discriminative feature selection (DFS) according to the uniqueness of pers...

In this paper, we review the problem of selecting relevant features for use in machine learning. We describe this problem in terms of heuristic search through a space of feature sets, and we identify four dimensions along which approaches to the problem can vary. We consider recent work on feature selection in terms of this framework, then close with some challenges for future work in the area....

Abdul Wahab, 1 Muhammad Faheem Khan, 2 Aurangzeb Khan , 3 Aziz Ullah Khan EDC, Gandhara University Peshawar,EDC, GU Peshawar,IECS UST Bannu,Dept. English UST Bannu [email protected], [email protected] ,[email protected], [email protected] ABSTRACT : The rapid growth of online blogs, social networks and other forums able the people and online users to discuss the variou...

In this paper, we review the problem of selecting relevant features for use in machine learning. We describe this problem in terms of heuristic search through a space of feature sets, and we identify four dimensions along which approaches to the problem can vary. We consider recent work on feature selection in terms of this framework, then close with some challenges for future work in the area....

We address the problem of nding a subset of features that allows a supervised induc tion algorithm to induce small high accuracy concepts We examine notions of relevance and irrelevance and show that the de nitions used in the machine learning literature do not adequately partition the features into useful categories of relevance We present de ni tions for irrelevance and for two degrees of rel...

{ A haptic object recognition methodology suitable for application with a multiingered robot hand is presented. The methodology exploits peculiar features of robot hands such as their distributed sensoriality and parallel kinematics for fast acquisition of contact data, and is based on volumetric representation models for eecient dynamic integration of the perceived information. Experimental re...

The evolving and adapting capabilities of robust intelligence are best manifested in its ability to learn. Machine learning enables computer systems to learn, and improve performance. Feature selection facilitates machine learning (e.g., classification) by aiming to remove irrelevant features. Feature (attribute) interaction presents a challenge to feature subset selection for classification. T...

As the storage technologies evolve, the amount of available data explodes in both dimensions: samples number and input space dimension. Therefore, one needs dimension reduction techniques to explore and to analyse his huge data sets. Many features selection approaches have been proposed for the supervised learning context, but only few techniques are available to address this issue in the unsup...

In this paper, we propose a novel method to select the most informative subset of features, which has little redundancy and very strong discriminating power. Our proposed approach automatically determines the optimal number of features and selects the best subset accordingly by maximizing the average pairwise informativeness, thus has obvious advantage over traditional filter methods. By relaxi...