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Statistical learning theory
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TLDR
Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.Abstract:
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.read more
Citations
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Journal ArticleDOI
An annual load forecasting model based on support vector regression with differential evolution algorithm
TL;DR: The effectiveness of this model has been proved by the final simulation which shows that the proposed model outperforms the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in the annual load forecasting.
Journal ArticleDOI
Model selection approaches for non-linear system identification: a review
TL;DR: A systematic overview of basic research on model selection approaches for linear-in-the-parameter models, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design is presented.
Journal ArticleDOI
Confidence-based active learning
Mingkun Li,Ishwar K. Sethi +1 more
TL;DR: This paper proposes a new active learning approach, confidence-based active learning, based on identifying and annotating uncertain samples, which takes advantage of current classifiers' probability preserving and ordering properties and is robust without additional computational effort.
Proceedings ArticleDOI
Optimising area under the ROC curve using gradient descent
Alan Herschtal,Bhavani Raskutti +1 more
TL;DR: This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC).
Journal ArticleDOI
Epileptic seizure prediction using relative spectral power features.
TL;DR: Applying machine learning methods on a reduced subset of proposed features could predict seizure onsets with high performance, and is of very low computational cost, while providing acceptable levels of alarm sensitivity and specificity.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?