Pattern Recognition and Machine Learning

@article{Neal2007PatternRA,
  title={Pattern Recognition and Machine Learning},
  author={Radford M. Neal},
  journal={Technometrics},
  year={2007},
  volume={49},
  pages={366 - 366}
}
the selection of symmetric factorial designs, that is, a design where all factors have the same number of levels. Chapter 3 focuses on selection of two-level factorial designs and discusses complementary design theory and related topics in the selection of designs. Chapter 4 covers the selection of three level designs followed by the general case of s-levels. Chapter 5 discusses estimation capacity, presenting the connections with complementary designs followed by the estimation capacity for… 
New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables
markdownabstractThis thesis discusses new mixture(-amount) models, choice models and the optimal design of experiments. Two chapters of the thesis relate to the so-called mixture, which is a product
Product Portfolio Selection of Designs Through an Analysis of Lower-Dimensional Manifolds and Identification of Common Properties
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Joint multitask feature learning and classifier design
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  • Computer Science
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Pattern Recognition and Machine Learning
sity, say, whereas least squares cross-validation can be considered a universally applicable method. The authors present an example for a single set of simulated data (from a bimodal density) to
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On efficient methods for high-dimensional statistical estimation
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Large-deviation analysis and applications Of learning tree-structured graphical models
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It is proved that among all unlabeled trees, the star and the chain are the worst and best for learning respectively, and scaling laws on the number of samples and the number variables for structure learning to remain consistent in high-dimensions are proved.
Bernoulli Mixture Models for Markov Blanket Filtering and Classification
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The use of Bernoulli mixture models for Markov blanket flltering and classiflcation of binary data is presented, overcoming the short comings of their algorithm and increasing the e‐ciency of this algorithm considerably.
Prototype Classification: Insights from Machine Learning
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This work sheds light on the discrimination between patterns belonging to two different classes by casting this decoding problem into a generalized prototype framework and relates mean-of-class prototype classification to other classification algorithms by showing that the prototype classifier is a limit of any soft margin classifier and that boosting a prototype classifiers yields the support vector machine.
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