Filtering Irrelevant Information for Rational Decision Making
01 Jan 2014-pp 111-130
TL;DR: Four methods for making rational decisions by either marginalizing irrelevant information or not using irrelevant information are discussed, which are marginalization of irrationality approach, automatic relevance determination, principal component analysis and independent component analysis.
Abstract: This chapter deals with the concept of using relevant information as a basis of rational decision making. In this regard, whenever information is irrelevant it needs to be marginalized or eliminated. Making decisions using information which contains irrelevant information often confuses a decision making process. In this chapter we discuss four methods for making rational decisions by either marginalizing irrelevant information or not using irrelevant information. These methods are marginalization of irrationality approach, automatic relevance determination, principal component analysis and independent component analysis. These techniques are applied to condition monitoring, credit scoring, interstate conflict and face recognition.
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Journal Article•
TL;DR: In this paper, a multi-unit deflation constraint independent component analysis (cICA) is proposed to estimate the contributions of the vibration sources via multiunit deflation constraints, which can not only improve the accuracy of the independent components and the robustness of the algorithm, but also obtain the corresponding relationships between the independent component and the vibration source.
Abstract: To obtain the main vibration sources of the mechanical systems, the method to estimate the contributions of the vibration sources via multi-unit deflation constraint independent component analysis(cICA) is proposed. In cICA, the prior knowledge of the source signals is introduced to the contrast function of the traditional independent component analysis(ICA) as constraints, which can not only improve the accuracy of the independent components and the robustness of the algorithm, but also obtain the corresponding relationships between the independent components and the vibration sources. To remove the components in mixed signals, which corresponds to the independent component extracted at each iteration, the multi-unit deflation approach is introduced to the cICA. In multi-unit deflation, the energy reduction of the mixed signals corresponds to the contribution of the extracted independent component. The proposed algorithm is applied to the shell excitation test bench and the cylindrical structure test bench, and then the mechanical vibration source separation and contribution calculation of the test benches are successfully completed.
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Book•
01 Jan 1995TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Abstract: From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
19,056 citations
TL;DR: In this article, the authors derived necessary conditions for any finite number of quanta and associated quantization intervals of an optimum finite quantization scheme to achieve minimum average quantization noise power.
Abstract: It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as the number of quanta becomes infinite, the asymptotic fractional density of quanta per unit voltage should vary as the one-third power of the probability density per unit voltage of signal amplitudes. In this paper the corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result as the number of quanta become large. The optimum quautization schemes for 2^{b} quanta, b=1,2, \cdots, 7 , are given numerically for Gaussian and for Laplacian distribution of signal amplitudes.
11,872 citations
10,702 citations