Erin Mills Town Centre Gift Cards are the perfect choice for your gift giving needs.Purchase gift cards at kiosks near the food court or centre court, at Guest Services, or click below to purchase online.PURCHASE HERE
Indigo Dimensionality Reduction with Unsupervised Nearest Neighbors | Erin Mills Town Centre
*Product information may vary - to confirm product availability, pricing, shipping and return information please contact Indigo
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.
The largest book retailer in Canada also offers toys, music, home décor, gifts and lifestyle products. What's Inside...Books, Magazines, CD’s and DVD’s, Toys and Gifts, Home Accents, Electronics, Baby’s and Children’s Section, Bath and Body, Kitchen and Bedroom, Stationary Located outside in the exterior plaza.
