We present an algorithm to overcome the local maxima problem in estimating the parameters of mixture models. It combines existing approaches from both EM and a robust fitting algorithm, RANSAC, to give a data-driven stochastic learning scheme. Minimal subsets of data points, sufficient to constrain the parameters of the model, are drawn from proposal densities to discover new regions of high li...

one of the most important and valuable goal of software development life cycle is software cost estimation or sce. during the recent years, sce has attracted the attention of researchers due to huge amount of software project requests. there have been proposed so many models using heuristic and meta-heuristic algorithms to do machine learning process for sce. cocomo81 is one of the most popular...

This paper presents a supervised machine learning approach that uses a machine learning algorithm called Random Forest for recognition of Bengali noun-noun compounds as multiword expression (MWE) from Bengali corpus. Our proposed approach to MWE recognition has two steps: (1) extraction of candidate multi-word expressions using Chunk information and various heuristic rules and (2) training the ...

Concrete is the second most consumed material after water and the most widely used construction material in the world. The compressive strength of concrete is one of its most important mechanical properties, which highly depends on its mix design. The present study uses the intelligent methods with instance-based learning ability to predict the compressive strength of concrete. To achieve this ...

the large amount of computation necessary for obtaining time optimal solution for moving a manipulator on specified path has made it impossible to introduce an on line time optimal control algorithm. most of this computational burden is due to calculation of switching points. in this paper a learning algorithm is proposed for finding the switching points. the method, which can be used for both ...

in this work we consider conjugacy of elements and parabolic subgroups‎ ‎in details‎, ‎in a new class of artin groups‎, ‎introduced in an earlier work‎, ‎which may contain arbitrary parabolic subgroups‎. ‎in particular‎, ‎we find‎ ‎algorithmically minimal representatives of elements in a conjugacy class‎ ‎and also an algorithm to pass from one minimal representative to the others‎.

This paper proposes a gradient ascent learning algorithm of the Hopfield neural networks for solving fixed linear crossing number problem. The fixed linear crossing number problem is an important problem in printed circuit board layout, VLSI circuit routing, and automated graph drawing. The objective of this problem which is shown to be NP-hard is to embed the edges so that the total number of ...

This paper addresses the problem of adaptive fuzzy tracking control for aclass of nonlinearly parameterized systems with unknown control directions.In this paper, the nonlinearly parameterized functions are lumped into the unknown continuous functionswhich can be approximated by using the fuzzy logic systems (FLS) in Mamdani type. Then, the Nussbaum-type function is used to de...

This paper proposes a minimal contrastive divergence method for learning energy-based generative ConvNet models of images at multiple grids (or scales) simultaneously. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized examples from th...