000			nam a22 7a 4500
999			_c28390 _d28390
008			170830b xxu||||| |||| 00| 0 eng d
020			_a9780262035644
082			_a515.392 _bHAZ
100			_aHazan, Tamir
245			_aPerturbations, optimization, and statistics
260			_bMIT Press; _c2016 _aCambridge:
300			_aviii, 401 p.; _bill.: _c25 cm.
365			_aUS$ _b60.00/ Rs. 4002.00
440			_aNeural information processing series
520			_aIn nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.
650			_aMachine learning
650			_aMathematical optimization
650			_aMathematics
650			_aNeural networks
650			_aComputer science
650			_aAlgorithms
942			_2ddc _cBK