Search Results

ENGLISH

Introduction To Lifted Probabilistic Inference

185,50€ + 10% CARD

 Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models.Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field.After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.ContributorsBabak Ahmadi, Hendrik Blockeel, Hung Bui, Yuqiao Chen, Arthur Choi, Jaesik Choi, Adnan Darwiche, Jesse Davis, Rodrigo de Salvo Braz, Pedro Domingos, Daan Fierens, Martin Grohe, Fabian Hadiji, Seyed Mehran Kazemi, Kristian Kersting, Roni Khardon, Angelika Kimmig, Jacek Kisynski, Daniel Lowd, Wannes Meert, Martin Mladenov, Raymond Mooney, Sriraam Natarajan, Mathias Niepert, David Poole, Scott Sanner, Pascal Schweitzer, Nima Taghipour, Guy Van den Broeck
ENGLISH

Log-Linear Models, Extensions, And Applications

251,75€ + 10% CARD

 Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives.Contributors Aleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg
ENGLISH

Perturbations, Optimization, And Statistics

185,50€ + 10% CARD

 A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees.
ENGLISH

Exploratory Analysis And Data Modeling In Functional Neuroimaging

95,40€ + 10% CARD

 An overview of theoretical and computational approaches to neuroimaging.Functional imaging tools such as fMRI (functional magnetic resonance imaging), PET (positron emission tomography), EEG (electro-encephalogram), and MEG (magneto-encephalogram) allow researchers to record activity in the working brain and draw inferences about how the brain functions. This book provides a survey of theoretical and computational approaches to neuroimaging, including inferential, exploratory, and causal methods of data analysis; theories of cerebral function; and biophysical and computational models of neural nets. It also emphasizes the close relationships between different approaches, for example, between causal data analysis and biophysical modeling, and between functional theories and computational models.
ENGLISH

Probabilistic Models Of The Brain - Perception And Neural Function

50,35€ + 10% CARD

 A survey of probabilistic approaches to modeling and understanding brain function.
ENGLISH

Advances In Minimum Description Length - Theory And Applications

99,38€ + 10% CARD

 A source book for state-of-the-art MDL, including an extensive tutorial and recent theoretical advances and practical applications in fields ranging from bioinformatics to psychology.
ENGLISH

Optimization For Machine Learning

159,00€ + 10% CARD

 An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.
ENGLISH

Toward Brain-Computer Interfacing

109,71€ + 10% CARD

 The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. This volume presents an overview of the BCI research. It covers a range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches.
ENGLISH

Predicting Structured Data

119,25€ + 10% CARD

 Presents and analyzes the state-of-the-art in machine learning algorithms and theory in machine learning. This volume contains contributions which discuss applications as diverse as machine translation, document markup, computational biology, and information extraction.
ENGLISH

Dataset Shift In Machine Learning

77,91€ + 10% CARD

 An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift.ContributorsShai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Bruckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Muller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Scholkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama
ENGLISH

Learning Machine Translation

84,27€ + 10% CARD

 The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This title investigates how Machine Learning techniques can improve Statistical Machine Translation.
ENGLISH

Practical Applications Of Sparse Modeling

161,65€ + 10% CARD

 Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision.
ENGLISH

Advanced Structured Prediction

172,25€ + 10% CARD

 An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs.The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Pruša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný
ENGLISH

Advances In Large-Margin Classifiers

101,76€ + 10% CARD

 The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research.
ENGLISH

Advanced Mean Field Methods - Theory And Practice - Theory And Practice

92,22€ + 10% CARD

 This book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling.
ENGLISH

Nearest-Neighbor Methods In Learning And Vision - Theory And Practice

119,25€ + 10% CARD

 Presents theoretical and practical discussions of nearest neighbour (NN) methods in machine learning. This book examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision.
ENGLISH

New Directions In Statistical Signal Processing - From Systems To Brains

132,50€ + 10% CARD

 Aims to promote the interaction and cross-fertilization between the fields of signal processing and neural computation. This book examines such topics as how computational models help us understand the brain's information processing, graphical and network structure modeling approaches, uncertainty in network communications, and more.
ENGLISH

Large-Scale Kernel Machines

132,50€ + 10% CARD

 Provides researchers and engineers practical solutions for learning from large scale datasets, with descriptions of algorithms and experiments carried out on realistically large datasets. This volume also offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms.
ENGLISH

Log-Linear Models, Extensions, And Applications

OUT OF STOCK OR NOT AVAILABLE
ENGLISH

The Phoenix Complex - A Philosophy Of Nature

by Michael Marder

OUT OF STOCK OR NOT AVAILABLE
ENGLISH

Gradient Expectations - Structure, Origins, And Synthesis Of Predictive Neural Networks

by Keith L. Downing

OUT OF STOCK OR NOT AVAILABLE
ENGLISH

Ownership Of Knowledge - Beyond Intellectual Property

OUT OF STOCK OR NOT AVAILABLE
ENGLISH

Life In Media - A Global Introduction To Media Studies

by Mark Deuze

OUT OF STOCK OR NOT AVAILABLE
ENGLISH

Molecular World - Making Modern Chemistry

by Catherine M. Jackson

OUT OF STOCK OR NOT AVAILABLE
Loading results...

View more results