Supervised support vector machines

More complex approaches to this problem involve the use of artificial neural networks [22], Bayesian networks [23] and support vector machines [24], which in turn are based on the same principle of supervised learning [25]. 

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. 

Abstract. Artificial neural networks (ANN) are useful components in today s data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits. 

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). 

MoA into one of the known MoAs of the GEP Compendium with a single experiment. Arahidopsis plants are sprayed with the respective compound and the isolated and labeled RNA is analyzed on the Arahidopsis chip. Subsequently, the resulting expression profile is compared with those in the compendium. This comparison is done by supervised learning algorithms like Support Vector Machine (SVM) [15] or Analysis of Variance (ANOVA) [16]. When the new expression profile groups together with profiles of a specific MoA in the compendium there is an utmost probability that the corresponding compound has the same MoA (Fig. 33.4). If necessary, the MoA can be verified by classical methods such as enzyme assays or supplementation tests, if available. 

A Support Vector Machine (SVM) is a class of supervised machine learning techniques. It is based on the principle of structural risk minimization. The ideal of SVM is to search for an optimal hyperplane to separate the data with maximal margin. Let <5 -dimensional input x belong to two classwhich was labeled... 

Support vector machine (SVM) is originally a binary supervised classification algorithm, introduced by Vapnik and his co-workers [13, 32], based on statistical learning theory. Instead of traditional empirical risk minimization (ERM), as performed by artificial neural network, SVM algorithm is based on the structural risk minimization (SRM) principle. In its simplest form, linear SVM for a two class problem finds an optimal hyperplane that maximizes the separation between the two classes. The optimal separating hyperplane can be obtained by solving the following quadratic optimization problem ... 

Selection of the supervised classification technique or the combination of techniques suitable for accomplishing the classification task. Popular supervised classifiers are Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN), Support Vector Machines (SVM), k-Nearest Neighbours (k-NN), combinations of genetic algorithms (GA) for feature selection with Linear Discriminant Analysis (LDA), Decision Trees and Radial Basis Function (RBF) classifiers. 

Such a diagnosis is obtained best using supervised or trained algorithms. A number of such predictive algorithms exist, some very similar to that of the principles of PCA (i.e. soft independent modeling of class analogy, SIMCA), linear discriminant analysis (LDA) to more compUcated classifiers such as support vector machines (SVMs), which are based on separating spectral classes by complicated, multidimensional separation planes [30]. At the LSpD, ANNs have been used [52-54] for supervised prediction of class memberships. 

In general, several tools have been developed to match reference spectra with those measured by an imaging spectrometer. The most common approach is based on the use of standard supervised classification techniques, where known spectra are used to determine the statistical properties of each class based on spectral characteristics. Examples of supervised classification approaches applied to hyperspectral data are described in McKeown et al. (1999) and Roessner et al. (2001), where the maximum likelihood classifier (MLC) was applied to map urban land cover. Other techniques are based on the use of support vector machines (SVM) (Melgani and Bruzzone 2004) and neural networks (NNs) (Licciardi et al. 2009, 2012). Other approaches have been designed explicitly for the analysis of imaging spectrometry data, such as the Spectral Angle Mapper (SAM Kruse et al. 1993). 

SVM/SVMR support vectors machine-regression—Unsupervised pattern recognition, supervised classification, quantization WLS weighted least squares—Spectral baseline correction... 

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