Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
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TLDR
This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.Abstract:
Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space En. The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.read more
Citations
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Journal ArticleDOI
On Fixed-Parameter Tractability and Approximability of NP Optimization Problems
Liming Cai,Jianer Chen +1 more
TL;DR: It is shown that an NP optimization problem is fixed-parameter tractable if it admits a fully polynomial-time approximation scheme, or if it belongs to the class MAX SNP or to theclass MIN F+?1, providing strong evidence that noW1-hard NP optimization problems belong to these optimization classes.
Journal ArticleDOI
Learnability with respect to fixed distributions
H. Balsters,Maarten M. Fokkinga +1 more
TL;DR: This paper shows how to adapt the semantics of a first order typed language, with semantics of S, in a simple set theoretic way, obtaining a semantics that satisfies, in addition to some obvious requirements, also the property that: $S'~s$ is included in S, whenever $s < t$.
Proceedings ArticleDOI
Query-preserving watermarking of relational databases and XML documents
TL;DR: This paper investigates the problem of watermarking databases or XML while preserving a set of parametric queries in a specified language, up to an acceptable distortion, and relates these results to an important topic in computational learning theory, the VC-dimension.
Posted Content
The limits of distribution-free conditional predictive inference
TL;DR: This work aims to explore the space in between exact conditional inference guarantees and what types of relaxations of the conditional coverage property would alleviate some of the practical concerns with marginal coverage guarantees while still being possible to achieve in a distribution-free setting.
Journal ArticleDOI
On-line retrainable neural networks: improving the performance of neural networks in image analysis problems
TL;DR: A novel approach for improving the performance of neural-network classifiers in image recognition, segmentation, or coding applications, based on a retraining procedure at the user level, which includes a maximum a posteriori estimation procedure for optimally selecting the most representative data of the current environment as retraining data.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book
The Art of Computer Programming
TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.