Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Result for publisher mit-press
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The...
Optimization for Machine Learning (Neural Information Processing series)
Optimization for Machine Learning (Neural Information Processing series)
The intersection of interests between machine learning and optimization has engaged many leading researchers in both communities for some years now. Both are vital and growing fields, and the areas of shared interest are expanding too. This volume collects contributions from many researchers who have been a part of these...
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed...
Knowledge-Based Neurocomputing
Knowledge-Based Neurocomputing
Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based...
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning...

Augmented Learning: Research and Design of Mobile Educational Games
Augmented Learning: Research and Design of Mobile Educational Games
New technology has brought with it new tools for learning, and research has shown that the educational potential of video games resonates with scholars, teachers, and students alike. In Augmented Learning, Eric Klopfer describes the largely untapped potential of mobile learning games--games played on such handheld devices as cell phones,...
2D Object Detection and Recognition: Models, Algorithms, and Networks
2D Object Detection and Recognition: Models, Algorithms, and Networks
This book is about detecting and recognizing 2D objects in gray-level images. Howare
models constructed? Howare they trained? What are the computational approaches to
efficient implementation on a computer? And finally, how can some of these computations
be implemented in the framework of parallel and biologically plausible neural...
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are...
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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 rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels--for a number of learning tasks. Kernel machines provide a modular framework...
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
"This is a fearless book that casts a wide net around key issues in cognitive science. It offers the kind of coherent, unified view that the field badly needs." - Steven Sloman, Associate Professor, Cognitive and Linguistic Sciences, Brown University"

Within cognitive science, two approaches currently dominate...
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure. In...
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Here we have a comprehensive, problem-oriented, engineering perspective on the uses of neural nets, fuzzy systems, and hybrids that emphasizes practical solutions to everyday artificial intelligence (AI) problems over abstract theoretical noodling. Intended for upper-division students and postgraduates who need a...
Result Page: 21 20 19 18 17 16 15 14 13 12 
©2020 LearnIT (support@pdfchm.net) - Privacy Policy