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Recognition of protein folding patterns is an important step in protein structure and function predictions. Traditional sequence similarity-based approach fails to yield convincing predictions when proteins have low sequence identities, while the taxonometric approach is a reliable alternative. From a pattern recognition perspective, protein fold(More)
We present an algorithm for the application of support vector machine (SVM) learning to image compression. The algorithm combines SVMs with the discrete cosine transform (DCT). Unlike a classic radial basis function networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of(More)
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Preface Swarm intelligence is an innovative computational way to solving hard problems. This(More)
Protein fold recognition task is important for understanding the biological functions of proteins. The adaptive local hyperplane (ALH) algorithm has been shown to perform better than many other renown classifiers including support vector machines, K-nearest neighbor, linear discriminant analysis, K-local hyperplane distance nearest neighbor algorithms and(More)
The chapter introduces the latest developments and results of Iterative Single Data Algorithm (ISDA) for solving large-scale support vector machines (SVMs) problems. First, the equality of a Kernel AdaTron (KA) method (originating from a gradient ascent learning approach) and the Sequential Minimal Optimization (SMO) learning algorithm (based on an analytic(More)
This paper presents the algorithms and the results of multi-user detectors (MUD) on a synchronous chaos-based code division multiple access system (CDMA), which uses chaotic sequences as the spreading codes. Popular linear and non-linear MUD algorithms such as the decorrelator detector, minimum mean square error (MMSE) detector and parallel interference(More)