Open Access
Pattern Recognition and Machine Learning
Christopher M. Bishop
- Vol. 738, Iss: 1
TLDR
Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.Abstract:
Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.read more
Citations
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Matrix Differential Calculus with Applications in Statistics and Econometrics
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Make3D: Learning 3D Scene Structure from a Single Still Image
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Reinforcement Learning in Robotics: A Survey.
Jens Kober,Jan Peters +1 more
TL;DR: A survey of work in reinforcement learning for behavior generation in robots can be found in this article, where the authors highlight key challenges in robot reinforcement learning as well as notable successes and discuss the role of algorithms, representations and prior knowledge in achieving these successes.
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Probabilistic machine learning and artificial intelligence
TL;DR: This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.
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Maximum likelihood from incomplete data via the EM algorithm
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Thomas M. Cover,Joy A. Thomas +1 more
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
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