Abstract: This paper presents a new approach for the prob-ability density function estimation using the Support Vector Ma-chines (SVM) and the Expectation Maximization (EM) algorithms.In the proposed approach, an advanced algorithm for the SVM den-sity estimation which incorporates the Mean Field theory in the learning process is used. Instead of using ad-hoc values for the para-meters of the kernel function which is used by the SVM algorithm,the proposed approach uses the EM algorithm for an automatic optimization of the kernel. Experimental evaluation using simulated data set shows encouraging results.
Abstract: Among neural models the Support Vector Machine
(SVM) solutions are attracting increasing attention, mostly because
they eliminate certain crucial questions involved by neural network
construction. The main drawback of standard SVM is its high
computational complexity, therefore recently a new technique, the
Least Squares SVM (LS–SVM) has been introduced. In this paper we
present an extended view of the Least Squares Support Vector
Regression (LS–SVR), which enables us to develop new
formulations and algorithms to this regression technique. Based on
manipulating the linear equation set -which embodies all information
about the regression in the learning process- some new methods are
introduced to simplify the formulations, speed up the calculations
and/or provide better results.
Abstract: This research was conducted for the first time at the
southeastern coasts of the Caspian Sea in order to evaluate the
performance of osteichthyes cooperatives through production (catch)
function. Using one of the indirect valuation methods in this research,
contributory factors in catch were identified and were inserted into
the function as independent variables. In order to carry out this
research, the performance of 25 Osteichthyes catching cooperatives
in the utilization year of 2009 which were involved in fishing in
Miankale wildlife refuge region. The contributory factors in catch
were divided into groups of economic, ecological and biological
factors. In the mentioned function, catch rate of the cooperative were
inserted into as the dependant variable and fourteen partial variables
in terms of nine general variables as independent variables. Finally,
after function estimation, seven variables were rendered significant at
99 percent reliably level. The results of the function estimation
indicated that human resource (fisherman quantity) had the greatest
positive effect on catch rate with an influence coefficient of 1.7 while
weather conditions had the greatest negative effect on the catch rate
of cooperatives with an influence coefficient of -2.07. Moreover,
factors like member's share, experience and fisherman training and
fishing effort played the main roles in the catch rate of cooperative
with influence coefficients of 0.81, 0.5 and 0.21, respectively.