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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Journal ArticleDOI
Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance
Till Rumpf,Anne-Kathrin Mahlein,Ulrike Steiner,Erich-Christian Oerke,H. W. Dehne,Lutz Plümer +5 more
TL;DR: A procedure for the early detection and differentiation of sugar beet diseases based on Support Vector Machines and spectral vegetation indices to discriminate diseased from non-diseased sugar beet leaves and to identify diseases even before specific symptoms became visible.
Journal ArticleDOI
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
TL;DR: The age manifold learning scheme for extracting face aging features is introduced and a locally adjusted robust regressor for learning and prediction of human ages is designed, which improves the age estimation accuracy significantly over all previous methods.
Proceedings Article
Pranking with Ranking
Koby Crammer,Yoram Singer +1 more
TL;DR: A simple and efficient online algorithm is described, its performance in the mistake bound model is analyzed, its correctness is proved, and it outperforms online algorithms for regression and classification applied to ranking.
Posted Content
Large Sample Sieve Estimation of Semi-Nonparametric Models
TL;DR: The method of sieves as discussed by the authors can be used to estimate semi-nonparametric econometric models with various constraints, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity.
Journal ArticleDOI
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
TL;DR: This work develops a double study, using different base classifiers in order to observe the suitability and potential of each combination within each classifier, and compares the performance of these ensemble techniques with the classifiers' themselves.
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?