Gaussian processes for machine learning /

Large-scale kernel machines /
Published: (2007)

Gaussian processes /
by: Hida, Takeyuki, 1927-2017
Published: (1993)

Neural networks and machine learning /
Published: (1998)

Concurrent learning and information processing : a neuro-computing system that learns during monitoring, forecasting, and control /
by: Jannarone, Robert J.
Published: (1997)

Large deviations for Gaussian queues : modelling communication networks /
by: Mandjes, Michel (Michael Robertus Hendrikus), 1970-
Published: (2007)

Semi-supervised learning /
Published: (2006)

Learning machine translation /
Published: (2009)

Learning kernel classifiers : theory and algorithms /
by: Herbrich, Ralf
Published: (2002)

Brian Uzzi discusses innovation with machine learning & neural networks.
Published: (2019)

Deep learning patterns and practices /
by: Ferlitsch, Andrew
Published: (2021)

Graphical models for machine learning and digital communication /
by: Frey, Brendan J.
Published: (1998)

Principles and labs for deep learning /
by: Huang, Shih-Chia
Published: (2021)

Gaussian processes : function theory, and the inverse spectral problem /
by: Dym, H. (Harry), 1938-
Published: (1976)

Learning with kernels : support vector machines, regularization, optimization, and beyond /
by: Schölkopf, Bernhard
Published: (2002)

An introduction to computational learning theory /
by: Kearns, Michael J.
Published: (1994)

Demystifying deep learning : an introduction to the mathematics of neural networks /
by: Santry, Douglas J.
Published: (2024)

Machine learning in non-stationary environments : introduction to covariate shift adaptation /
by: Sugiyama, Masashi, 1974-
Published: (2012)

Machine learning for data streams : with practical examples in MOA /
by: Bifet, Albert, et al.
Published: (2017)

Applications of neural networks and machine learning in image processing IX : 19-20 January 2005, San Jose, California, USA /
Published: (2005)

Stochastic analysis for gaussian random processes and fields : with applications /
by: Mandrekar, V. (Vidyadhar), 1939, et al.
Published: (2015)

Bioinformatics : the machine learning approach /
by: Baldi, Pierre
Published: (1998)

Learning, networks and statistics /
Published: (1997)

Stochastic analysis of mixed fractional Gaussian processes.
by: Mishura, I︠U︡lii︠a︡ S.
Published: (2018)

Machine learning with neural networks : an introduction for scientists and engineers /
by: Mehlig, Bernhard, 1964-
Published: (2021)

Stochastic analysis for Gaussian random processes and fields : with applications /
by: Mandrekar, V. (Vidyadhar), 1939-, et al.
Published: (2016)

Nearest-neighbor methods in learning and vision : theory and practice /
Published: (2005)

Learning from data : concepts, theory, and methods /
by: Cherkassky, Vladimir S.
Published: (2007)

Machine Learning for Auditors : Automating Fraud Investigations Through Artificial Intelligence /
by: Sekar, Maris
Published: (2022)

Machine learning for future wireless communications /
Published: (2020)

Machine learning for physics and astronomy /
by: Acquaviva, Viviana
Published: (2023)

Bioinformatics : the machine learning approach /
by: Baldi, Pierre
Published: (2001)

Advances in Bayesian networks /
Published: (2004)

Bayesian artificial intelligence /
by: Korb, Kevin B.
Published: (2004)

Machine learning in chemistry : data-driven algorithms, learning systems, and predictions /
Published: (2019)

Machine Learning : The Basics /
by: Jung, Alexander
Published: (2022)

Statistical Inference and Machine Learning for Big Data /
by: Alvo, Mayer
Published: (2022)