In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states – confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed exten...

Heterogeneous and aggregate vectors are the two widely used feature vectors in fixed text keystroke authentication. In this paper, we address the question ‘‘Which vectors, heterogeneous, aggregate, or a combination of both, are more discriminative and why?’’ We accomplish this in three ways – (1) by providing an intuitive example to illustrate how aggregation of features inherently reduces disc...

Feature extraction is a key technology in the process of the 3D model retrieval, and the retrieval result is determined by the quality of the features. This paper structures one function (θ, φ,H) which is used the spatial position (θ, φ) and the mean curvature (H) of the 3D model surface point. Through making harmonic analysis for the function, a group of partial features which have rotation in...

Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used to map the input space into a high dimensional feature space. However, it can perform rather poorly when there are too many dimensions (e.g. for gene expression data) or when there is a lot of noise. In this paper, we ...

We consider the matching function in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the similarity of the unknown speaker and the models in the database. In this paper, we propose to use weighted matching method that takes into account ...

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...

This paper introduces the Graph Embedded One-Class Support Vector Machine and Graph Embedded Support Vector Data Description methods. These methods constitute novel extensions of the One-Class Support Vectors Machines and Support Vector Data Description, incorporating generic graph structures that express geometric data relationships of interest in their optimization process. Local or global re...

The Support vector machines derive the class decision hyper planes from a few, selected prototypes, the support vectors (SVs) according to the principle of structure risk minimization, so they have good generalization ability. We proposed a new prototype selection method based on support vectors for nearest neighbor rules. It selects prototypes only from support vectors. During classification, ...

In this paper, a feature-preserving interpolative vector quantization method is proposed to compress images. The proposed method preserves the average color and color contrast on generating the approximation image and preserves mean, variance, and average radius on quantizing the residual image which is generated by subtracting the approximation image from the source image. The experimental res...

Feature selection has become an increasingly important field of research. It aims at finding optimal feature subsets that can achieve better generalization on unseen data. However, this can be a very challenging task, especially when dealing with large feature sets. Hence, a search strategy is needed to explore a relatively small portion of the search space in order to find ”semi-optimal” subse...