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In machine learning support-vector machines (SVMs also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.Given a set of training examples each marked as belonging to one or the other of two categories an SVM training algorithm builds a model that

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Abstract. Motivation Classification and feature selection of genomics or transcriptomics data is often hampered by the large number of features as compared with the small number of samples available. Moreover features represented by probes that either have similar molecular functions (gene expression analysis) or genomic locations (DNA copy number analysis) are highly correlated.

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Jan 10 2013 · We describe a quantum algorithm that generalizes the quantum linear system algorithm Harrow et al. Phys. Rev. Lett. 103 150502 (2009) to arbitrary problem specifications. We develop a state preparation routine that can initialize generic states show how simple ancilla measurements can be used to calculate many quantities of interest and integrate a quantum-compatible preconditioner that

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Also linear classifiers often work very well when the number of dimensions in → is large as in document classification where each element in → is typically the number of occurrences of a word in a document (see document-term matrix). In such cases the classifier

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Linear logic is a refinement of classical and intuitionistic logic. Instead of emphasizing truth as in classical logic or proof as in intuitionistic logic linear logic emphasizes the role of formulas as Linear logic is a refinement of classical and intuitionistic logic. Instead of emphasizing truth as in classical logic or proof as in intuitionistic logic linear logic emphasizes the role of formulas as resources.To achieve this focus linear logic does not allow the usual structural rules of contraction and weakening to apply to all formulas but only those formulas marked with certain modals.Get Price### TPS7A52-Q1 Automotive 2-A low-VIN (1.1-V) low-noise

The TPS7A52-Q1 device is a low-noise (4.4 µV RMS) low-dropout linear regulator (LDO) capable of sourcing 2 A with only 115 mV of maximum dropout.The device output voltage is adjustable from 0.8 V to 5.2 V using an external resistor divider. The combination of low-noise (4.4 µV RMS) high-PSRR and high output current capability makes the TPS7A52-Q1 ideal to power noise-sensitive components

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Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks they have in most cases the disadvantage of acting as a black box

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Jan 10 2013 · We describe a quantum algorithm that generalizes the quantum linear system algorithm Harrow et al. Phys. Rev. Lett. 103 150502 (2009) to arbitrary problem specifications. We develop a state preparation routine that can initialize generic states show how simple ancilla measurements can be used to calculate many quantities of interest and integrate a quantum-compatible preconditioner that

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Four different classification algorithms namely naïve Bayes support vector machines k-nearest neighbour and linear discriminant analysis are used for the study. Classifier performances with the MTW features are compared with the currently used time- and frequency-domain features.

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Abstract. Motivation Classification and feature selection of genomics or transcriptomics data is often hampered by the large number of features as compared with the small number of samples available. Moreover features represented by probes that either have similar molecular functions (gene expression analysis) or genomic locations (DNA copy number analysis) are highly correlated.

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