Discriminant analysis, support vector

S.J. Dixon and R.G. Brereton, Comparison of performance of five common classifiers represented as boundary methods Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure, Chemom. Intell. Lab. Syst, 95, 1-17 (2009). 

Chang RF, Wu WJ, Moon WK et al (2003) Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med Biol 29 679-686... 

Various classification approaches have been reported to be used successfully in conjunction with fragment descriptors for building classification SAR models the Linear Discriminant Analysis (LDA), the Partial Least Square Discriminant Analysis (PLS-DA), Soft Independent Modeling by Class Analogy (SIMCA), Artificial Neural Networks (ANN), ° Support Vector... 

Luan F, Zhang R, Zhao C, Yao X, Liu M, et al. Classification of the carcinogenicity of /V-nitroso compounds based on support vector machines and linear discriminant analysis. Chem Res Toxicol 2005 18 198-203. 

LH Chiang, ME Kotanchek, and AK Kordon. Fault diagnosis based on Fisher s discriminant analysis and support vector machines. Corn-put Chem. Engg., 28(8) 1389-1401, 2004. 

T Van Gestel, J Suykens, G Lanckriet, A Lambrechts, B De Moor, and J Vandewalle. Bayesian framework for least squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis. Neural Computation, 15 1115-1148, 2002. 

The most popular classification methods are Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA), Kth Nearest Neighbors (KNN), classification tree methods (such as CART), Soft-Independent Modeling of Class Analogy (SIMCA), potential function classifiers (PFC), Nearest Mean Classifier (NMC), Weighted Nearest Mean Classifier (WNMC), Support Vector Machine (SVM), and Classification And Influence Matrix Analysis (CAIMAN). 

A whole spectrum of statistical techniques have been applied to the analysis of DNA microarray data [26-28]. These include clustering analysis (hierarchical, K-means, self-organizing maps), dimension reduction (singular value decomposition, principal component analysis, multidimensional scaling, or correspondence analysis), and supervised classification (support vector machines, artificial neural networks, discriminant methods, or between-group analysis) methods. More recently, a number of Bayesian and other probabilistic approaches have been employed in the analysis of DNA microarray data [11], Generally, the first phase of microarray data analysis is exploratory data analysis. 

Similar to protein-DNA interactions, protein-RNA interactions also perform vital roles in the cell including protein synthesis, viral replication, cellular defense and developmental regulation [145,146]. One major direction in the analysis of protein-RNA interactions is to identify proteins that bind RNA based on features derived from physio-chemical properties of the sequence. A number of published works have focused casting this problem as a binary classification problem using the support vector machines (SVM) classifier to identify proteins that bind RNA [33-35, 51]. Each of these works derived large data sets from the SwissProt database and applied the support vector machines classifier to discriminate protein sequences that bind RNA from all other sequences. Since sequence analysis techniques can identity homologous proteins as having similar function, most of these works reduced the redundancy of the data sets below a certain threshold <40% [35], < 25%... 

A later chapter will discuss these methods in more detail. For example, support vector machines and traditional neural networks are analogs of multiple regression or discriminant analysis that provide more flexibility in the form of the relationship between molecular properties and bioactivity.Kohonen neural nets are a more flexible analog to principal component analysis. Various Bayesian approaches are alternatives to the statistical methods described earlier. A freely available program oflcrs many of these capabilities. ... 

If the membership of objects to particular clusters is known in advance, the methods of supervised pattern recognition can be used. In this section, the following methods are explained linear learning machine (LLM), discriminant analysis, A -NN, the soft independent modeling of class analogies (SIMCA) method, and Support Vector Machines (SVMs). 

In contrast to conventional classification methods such as discriminant analysis, no assumptions about the form of the underlying class distributions are necessary with support vector classifiers. 

There are a number of classification methods for analyzing data, including artificial neural (ANNs see Beale and Jackson, 1990) networks, -nearest-neighbor (fe-NN) methods, decision trees, support vector machines (SVMs), and Fisher s linear discriminant analysis (LDA). Among these methods, a decision tree is a flow-chart-like tree stmcture. An intermediate node denotes a test on a predictive attribute, and a branch represents an outcome of the test. A terminal node denotes class distribution. 

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