In this paper we present an efficient system for independent speaker speech recognition based on neural network approach. The proposed architecture comprises two phases: a preprocessing phase which consists in segmental normalization and features extraction and a classification phase which uses neural networks based on nonparametric density estimation namely the general regression neural networ...

We had the opportunity to conduct an empirical study in the context of the testing environment for a large commercial product. The particular goal of the organization for which this study was done, was to gain a strong understanding of how particular aspects of their testing practice impact on the quality of the released products. In this paper we present some of the results of that research as...

Generalized Regression Neural Network (GRNN) is usually applied to the Function approximation. This paper, based on the principle of GRNN, presents a method for the predictive model of nonlinear complex system. The presented algorithm is applied to the learning and predicting process for the system modeling. The simulations show the described method has good effects on predicting the dynamic pr...

The aim of this research is to develop an autonomous system for solving data analysis problems. system, called Genetic Programming-Autonomous Solver (GP-AS) contains most of the features requ by an autonomous software: it decides if it knows or not how to solve a particular problem, it construct solutions for new problems, it can store the created solutions for later use, it can improve existin...

Techniques for using multiple regression (MR) as a general variance-accounting procedure of great flexibility, power, and fidelity to research aims in both manipulative and observational psychological research are presented. As a prelude, the identity of MR and fixed-model analysis of variance/covariance (AV/ACV) is sketched. This requires an exposition of means of expressing nominal scale (qua...

The classification and regression trees (CART) possess the advantage of being able to handlelarge data sets and yield readily interpretable models. In spite to these advantages, they are alsorecognized as highly unstable classifiers with respect to minor perturbations in the training data.In the other words methods present high variance. Fuzzy logic brings in an improvement in theseaspects due ...

We derive a cost functional for estimating the inverse of the observation function in nonlinear dynamical systems. Limiting our search to invertible observation functions confers numerous benefits, including a compact representation and no local minima. Our approximation algorithms for optimizing this cost functional are fast, and give diagnostic bounds on the quality of their solution. Our met...

There is a constant need for practical, efficient and costeffective software evolution techniques. We propose a novel evolution methodology that integrates the concepts of features and component-based software engineering (CBSE). We collect information about a legacy system’s features through interviews with key developers, users of the system and analyzing the existing regression test cases. W...

This paper describes the automatic MT evaluation system by Machine Intelligence and Translation Lab. of Harbin Institute of Technology, for NIST MetricsMATR 2008 evaluation. The system is based on SVM regression and employed many linguistic features. Machine-learning based automatic MT evaluation has recently gained more attention in NLP research. And feature mining is essential for ML. In this...

In this paper, we present an expert system, called PASS (Predicting Ability of Students to Succeed), which is used to predict how certain is that a student of a specific type of high school in Greece will pass the national exams for entering a higher education institute. Prediction is made at two points. An initial prediction is made after the second year of studies and the final after the end ...