Open Access
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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A similarity learning approach to content-based image retrieval: application to digital mammography
TL;DR: A new approach to content-based retrieval of medical images from a database is described, in which similarity is learned from training examples provided by human observers, and the use of neural networks and support vector machines to predict the user's notion of similarity is explored.
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On regularization algorithms in learning theory
TL;DR: It is shown that a notion of regularization defined according to what is usually done for ill-posed inverse problems allows to derive learning algorithms which are consistent and provide a fast convergence rate.
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Machine Learning in Medical Imaging
TL;DR: This article will discuss very different ways of using machine learning that may be less familiar, and will demonstrate through examples the role of these concepts in medical imaging.
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A support vector machine–firefly algorithm-based model for global solar radiation prediction
Lanre Olatomiwa,Lanre Olatomiwa,Saad Mekhilef,Shahaboddin Shamshirband,Kasra Mohammadi,Dalibor Petković,Ch. Sudheer +6 more
TL;DR: In this article, a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined, which is developed by hybridizing the Support Vector Machines (SVMs) with Firefly Algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration (n¯), maximum temperature (Tmax), and minimum temperature(Tmin) as inputs.
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An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data
TL;DR: Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs, and the estimated results of the proposed PV power forecasting model coincide well with measurement data as discussed by the authors.
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?