# Machine learning - a probabilistic perspective

@inproceedings{Murphy2012MachineL, title={Machine learning - a probabilistic perspective}, author={Kevin P. Murphy}, booktitle={Adaptive computation and machine learning series}, year={2012} }

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as…

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## References

SHOWING 1-10 OF 1,032 REFERENCES

### Gaussian Processes for Machine Learning

- Computer ScienceAdaptive computation and machine learning
- 2009

The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.

### Probabilistic Graphical Models - Principles and Techniques

- Computer Science
- 2009

The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.

### Computer Vision: Models, Learning, and Inference

- Computer Science
- 2012

This modern treatment of computer vision shows how to use training data to learn the relationships between the observed image data and the aspects of the world that the authors wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data.

### Learning Determinantal Point Processes

- Computer ScienceUAI
- 2011

This thesis shows how determinantal point processes can be used as probabilistic models for binary structured problems characterized by global, negative interactions, and demonstrates experimentally that the techniques introduced allow DPPs to be used for real-world tasks like document summarization, multiple human pose estimation, search diversification, and the threading of large document collections.

### Inducing Features of Random Fields

- Computer ScienceIEEE Trans. Pattern Anal. Mach. Intell.
- 1997

The random field models and techniques introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated.

### Probabilistic models of vision and max-margin methods

- Computer Science
- 2012

This paper shows that by placing bounds on the normalization constant the authors can obtain computationally tractable approximations to probabilistic methods including multi-class max- margin, ordinal regression, max-margin Markov networks and parsers, multiple-instance learning, and latent SVM.

### Efficiently Inducing Features of Conditional Random Fields

- Computer ScienceUAI
- 2003

This paper presents an efficient feature induction method for CRFs founded on the principle of iteratively constructing feature conjunctions that would significantly increase conditional log-likelihood if added to the model.

### Large-scale kernel machines

- Computer Science
- 2007

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

### Max-Margin Markov Networks

- Computer ScienceNIPS
- 2003

Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data, and a new theoretical bound for generalization in structured domains is provided.