Book Chapter10.1007/978-3-540-68860-0_1
A new framework for machine learning
Christopher M. Bishop
- 01 Jun 2008
- pp 1-24
TL;DR: The last five years have seen the emergence of a powerful new framework for building sophisticated real-world applications based on machine learning, which combines the adoption of a Bayesian viewpoint, use of graphical models to represent complex probability distributions, and the development of fast, deterministic inference algorithms.
read more
Abstract: The last five years have seen the emergence of a powerful new framework for building sophisticated real-world applications based on machine learning. The cornerstones of this approach are (i) the adoption of a Bayesian viewpoint, (ii) the use of graphical models to represent complex probability distributions, and (iii) the development of fast, deterministic inference algorithms, such as variational Bayes and expectation propagation, which provide efficient solutions to inference and learning problems in terms of local message passing algorithms. This paper reviews the key ideas behind this new framework, and highlights some of its major benefits. The framework is illustrated using an example large-scale application.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Generative adversarial networks: introduction and outlook
TL;DR: It is concluded that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration, and can provide substantial algorithmic support for parallel intelligence.
682
Blockchain-Powered Parallel Healthcare Systems Based on the ACP Approach
TL;DR: The emerging blockchain technology with PHS is combined, via constructing a consortium blockchain linking patients, hospitals, health bureaus, and healthcare communities for comprehensive healthcare data sharing, medical records review, and care auditability.
325
Capturing Car-Following Behaviors by Deep Learning
TL;DR: A deep neural network-based car-following model that takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs and tries to embed prediction capability or memory effect of human drivers in a natural and efficient way.
325
Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence
TL;DR: The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
Lefeng Cheng,Tao Yu +1 more
TL;DR: This survey focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields.
270
References
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
30.8K
A tutorial on hidden Markov models and selected applications in speech recognition
Lawrence R. Rabiner
- 01 Feb 1989
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
•Book
Pattern Recognition and Machine Learning
Christopher M. Bishop
- 17 Aug 2006
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
A New Approach to Linear Filtering and Prediction Problems
Tamer Basar
- 01 Jan 2001
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
22.7K