An Information-Theoretic Solution to Parameter Setting*

The Institute For Research In Cognitive Science An Information - Theoretic Solution for Parameter Setting

Application of statistical techniques and artificial neural network to estimate force from sEMG signals

This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best ...

Statistical Decisions Using Likelihood Information Without Prior Probabilities

This paper presents a decision-theoretic approach to statistical inference that satisfies the Likelihood Principle (LP) without using prior information. Unlike the Bayesian approach, which also satisfies LP, we do not assume knowledge of the prior distribution of the unknown parameter. With respect to information that can be obtained from an experiment, our solution is more efficient than Wald’...

A new last aggregation compromise solution approach based on TOPSIS method with hesitant fuzzy setting to energy policy evaluation

Utilizing renewable energies is identified as one of significant issues for economical and social significance in future human life. Thus, choosing the best renewable energy among renewable energy candidates is more important. To address the issue, multi-criteria group decision making (MCGDM) methods with imprecise information could be employed to solve these problems. The aim of this paper is ...

An Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition

Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...