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2002, Adaptive computation and machine learning, ISBN 0262194759, xviii, 626
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave... 
Algorithms | Kernel functions | Mathematical optimization | Machine learning | Computing and Processing | Support vector machines
Book
2004, ISBN 0262195097, ix, 400
Book
2006, Adaptive computation and machine learning, ISBN 0262033585, x, 508
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are... 
Supervised learning (Machine learning) | Applications of Computing | Computing and Processing
Book
2005, Information science and statistics, ISBN 1852338911, xvi, 285
Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures -- the author uses... 
Applications of Computing | Statistics | Distribution (Probability theory) | Mathematical models | Decision making | Artificial intelligence | Graphic methods | Artificial Intelligence (incl. Robotics) | Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences | Computer Science
Book
Journal Article
Nature, ISSN 0028-0836, 02/2015, Volume 518, Issue 7540, pp. 486 - 487
On page 529 of this issue, Mnih et al.1 describe an agent that uses large data sets to teach itself how to play 49 classic Atari 2600 computer games by looking... 
Usage | Research | Artificial intelligence | Machine learning | Datasets | Problems | Algorithms | Computer & video games
Journal Article
Machine Learning, ISSN 0885-6125, 9/2019, Volume 108, Issue 8, pp. 1329 - 1351
Journal Article
Nature Genetics, ISSN 1061-4036, 05/2005, Volume 37, Issue 5, pp. 501 - 506
Journal Article
IEEE Transactions on Information Theory, ISSN 0018-9448, 10/2010, Volume 56, Issue 10, pp. 5168 - 5194
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 07/2016, Volume 38, Issue 7, pp. 1439 - 1451
We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural... 
Training | Deconvolution | Convolution | Sharpening and deblurring | Estimation | Artificial neural networks | neural networks | Feature extraction | machine learning | Kernel | IMAGE | BLIND DECONVOLUTION | NETWORKS | BLUR IDENTIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
Nature, ISSN 0028-0836, 02/2015, Volume 518, Issue 7540, pp. 486 - 487
Journal Article
Journal of the Royal Statistical Society: Series B (Statistical Methodology), ISSN 1369-7412, 01/2018, Volume 80, Issue 1, pp. 5 - 31
Summary We investigate the problem of testing whether d possibly multivariate random variables, which may or may not be continuous, are jointly (or mutually)... 
Causal inference | V‐statistics | Independence test | Kernel methods | V-statistics | DISTANCE | BOOTSTRAP | CONSISTENT TEST | STATISTICS & PROBABILITY | Permutations | Hilbert space | Random variables | Criteria | Independent variables
Journal Article