نتایج جستجو برای: learning algorithm
تعداد نتایج: 1264648 فیلتر نتایج به سال:
The Infomax algorithm is a popular method in blind source separation problem. In this article an extension of the Infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-Gaussian distributions. This ability is the results of using two different nonlinear functions and new coefficients in the learning rule. In this paper we show how we can use the distribution...
Meta-heuristics optimization methods are important techniques for optimal design of the engineering systems. Numerous methods, inspired by different nature phenomena, have been introduced in the literature. A new modified version of Teaching-Learning-Based Optimization (TLBO) Algorithm is introduced in this paper. TLBO, as a parameter free algorithm, is based on the learning procedure of studen...
The goal of recommender system is to provide desired items for users. One of the main challenges affecting the performance of recommendation systems is the cold-start problem that is occurred as a result of lack of information about a user/item. In this article, first we will present an approach, uses social streams such as Twitter to create a behavioral profile, then user profiles are clusteri...
Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown t...
The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the manifests that only implementation of MMHC in statistical software R is prohibitively expensive, and offered. specifically for case data, robust outliers version FEDHC, which can be adopted by othe...
A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG). There are basically two methods used for learning Bayesian network: parameter-learning and structure-learning. One of the most effective structure-learning methods is K2 algorithm. Because the performance of the K2 algorithm depends on node...
E-learning model is examined of three major dimensions. And each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. If any of these undetermined events be considered in the optimization process, t...
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