In this paper we show that efficient object recognition can be obtained by combining informative features with linear classification. The results demonstrate the superiority of informative class-specific features, as compared with generic type features such as wavelets, for the task of object recognition. We show that information rich features can reach optimal performance with simple linear se...

We address a situation when more than one feature subset allows for linear separability of given data sets. Such situation can occur if a small number of cases is represented in a highly dimensional feature space. The method of the feature selection based on minimisation of a special criterion function is here analysed. This criterion function is convex and piecewise-linear (CPL). The proposed ...

The fuzzy linear regression model with fuzzy input-output data andcrisp coefficients is studied in this paper. A linear programmingmodel based on goal programming is proposed to calculate theregression coefficients. In contrast with most of the previous works, theproposed model takes into account the centers of fuzzy data as animportant feature as well as their spreads in the procedure ofconstr...

Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...

In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...

We propose a new algorithm called Generalized Discriminative Feature Transformation (GDFT) for acoustic models in speech recognition. GDFT is based on Lagrange relaxation on a transformed optimization problem. We show that the existing discriminative feature transformation methods like feature space MMI/MPE (fMMI/MPE), region dependent linear transformation (RDLT), and a non-discriminative feat...

Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...

We show that Integer Programming (IP) can be used as an optimization technique for the instantiation of products of feature models. This is done by showing that the constraints of feature models can be written in linear form. As particular IP technique, we use Gomory cutting planes. We have applied this technique to a test suite of feature models from the literature, and found that the Gomory c...

Feature extraction is the key element when aiming at robust speech recognition. In this work both linear and nonlinear data-driven feature transformations were applied to the logarithmic mel-spectral context feature vectors in the TIMIT phone recognition task. Transformations were based on Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LD...

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively represent a salient image region while remaining invariant to viewpoint and illumination changes. This representation can be improved using machine learning...