An introduction to kernel-based learning algorithms

Müller, Klaus‐Robert; Mika, Sebastian; Rätsch, Gunnar; Tsuda, Koji; Schölkopf, Bernhard

doi:10.1109/72.914517

Cited by 3,029 publications

(1,202 citation statements)

References 89 publications

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“…Nonlinear methods to compute Fisher discriminants have been developed in recent years using kernel-based approaches (Muller et al 2001). Similarly, one might ask whether using nonlinear dynamical EEG measures instead of our linear ones might have improved the results of our linear or an alternative nonlinear discrimination method.…”

Section: Discussionmentioning

confidence: 99%

Neuronal spatiotemporal pattern discrimination: The dynamical evolution of seizures

Kumar²,

2005

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We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience -whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that human seizures have distinct initiation and termination dynamics, an important characterization as we seek to better understand how seizures start and stop. Our approach is broadly applicable to a wide variety of neuronal data, from multichannel EEG or MEG, to sequentially acquired optical imaging data or fMRI.

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Section: Discussionmentioning

confidence: 99%

Neuronal spatiotemporal pattern discrimination: The dynamical evolution of seizures

Kumar²,

2005

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“…Typically cross-validation techniques are applied here, where an appropriate range of the hyperparameters is scanned (e.g. ref 7). A proper model selection is the key to a successful application of modern learning techniques; however, at the same time it is typically computationally very expensive.…”

Section: Methodsmentioning

confidence: 99%

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Müller

Rätsch

Sonnenburg

et al. 2005

J. Chem. Inf. Model.

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In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process.

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“…The most frequently used kernel functions are the polynomial kernel, the sigmoid kernel, and the radial basis kernel functions (RBF) [31][32][33]. Since the RBF is usually used in classification using support vector machines [34] and was found by Huang et al [21] to exhibit the best performance, it is used in this work.…”

Section: Classification By Svmmentioning

confidence: 99%

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

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A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96. 67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

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An introduction to kernel-based learning algorithms

Cited by 3,029 publications

References 89 publications

Neuronal spatiotemporal pattern discrimination: The dynamical evolution of seizures

Neuronal spatiotemporal pattern discrimination: The dynamical evolution of seizures

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

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