dimensional data available from public financial statements make credit analysis difficult. To address the problem‚ dimensionality reduction is a key step to enhance scoring accuracy. By using semi-supervised discriminant analysis (SSDA) and support vector machines (SVMs)‚ this study develops a novel system for credit scoring‚ where SSDA transforms high dimensional data space (over 50 financial variables) to a perfect low dimensional representative subspace with maximal discriminating power. Constructing
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Adaptive Filters 1 Adaptive Filters A Tutorial for the Course Computational Intelligence http://www.igi.tugraz.at/lehre/CI Christian Feldbauer‚ Franz Pernkopf‚ and Erhard Rank Signal Processing and Speech Communication Laboratory Inffeldgasse 16c Abstract This tutorial introduces the LMS (least mean squares) and the RLS (recursive least-squares) algorithm for the design of adaptive transversal filters. These algorithms are applied for identification of an unknown system. Usage
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1 Chapter 4.1 Marginal Functions in Economics ___________ Cost: Suppose that C ( x ) describes the cost function for producing x number of a certain product. Then the ___________ cost is the derivative of the cost function‚ C ( x) ‚ and measures the rate of ________ of the cost function ______________ the number of units ______________. Note 1: The marginal cost for a particular value of x is the ___________ cost of one __________ unit of production. ___________ Revenue Function: R( x) px
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4/28/2008 Spatial filtering fundamentals by Gleb V. Tcheslavski: gleb@ee.lamar.edu http://ee.lamar.edu/gleb/dip/index.htm Spring 2008 ELEN 4304/5365 DIP 1 Mechanics of spatial filtering Considering frequency domain filtering‚ the effect of LPF applied to an image is to blur (smooth) it. Similar smoothing effect can be achieved by using spatial filters (spatial masks‚ kernels‚ templates‚ or windows). We discussed that a spatial filter consists of a neighborhood and a pre-defined operation
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Neurocomputing 55 (2003) 307 – 319 www.elsevier.com/locate/neucom Financial time series forecasting using support vector machines Kyoung-jae Kim∗ Department of Information Systems‚ College of Business Administration‚ Dongguk University‚ 3-26‚ Pil-dong‚ Chung-gu‚ Seoul 100715‚ South Korea Received 28 February 2002; accepted 13 March 2003 Abstract Support vector machines (SVMs) are promising methods for the prediction of ÿnancial timeseries because they use a risk function consisting of the
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Working On Your Essay Introduction: (150 words) 1. State your central claim Society will be in anarchy if the For example a murder can claim to have gotten pleasure out of killing someone – society cannot function if individuals perceptive view of pleasure are deemed morally correct. Another example is the act of rape were it is seen as a pleasurable act for the individual performing the act however this 2. two arguments used to support the claim. 3
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investigate the performance of novel classification schemes for spectrum sensing in cooperative multiple-input multiple-output (MIMO) wireless cognitive radio (CR) networks. In this context‚ we consider several optimal classification schemes such as support vector classifiers (SVC)‚ logistic regression (LR) and quadratic discrimination (QD) for primary user detection. It is demonstrated that these classification techniques have a significantly reduced complexity of implementation in practical CR applications
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com Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naïve Bayes and SVM. The main advantages of the method are that it is simple‚ computationally efficient
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FINAL YEAR PROJECT INTERIM REPORT COMPUTATIONALLY ASSESSING THE VISUAL QUALITY OF A WEB PAGE (A3147-121) Supervised by Submitted by : Prof. Wang Gang : Aparna Janardhanan Nambiar U0920595J IEM/4 TABLE OF CONTENTS Table of Figures ................................................................................................................................................................. 2 Abstract ..................................................................................
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many mathematical discoveries that have influenced our way of thinking‚ perhaps‚ the invention of Calculus should be considered one of the greatest achievements of mankind (Allen‚ page 1). The discovery of calculus has been a major time point in history. It has solved many mathematical problems as well as contributing to other scientific areas such as physics‚ electricity‚ and engineering. Calculus can be attributed to two famous scholars – Isaac Newton and Gottfried Wilhelm Leibnitz. Newton’s contributions
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