Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

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Multiple View Geometry in Computer Vision.

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth

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Simultaneous inference in general parametric models.

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

https://www.wsl.ch/staff/niklaus.zimmermann/papers/ecomod135_147.pdf

Predictive habitat distribution models in ecology

1. Introduction. 2. Simple Regression. 3. Multiple Regression. 4. The Special Case of One-Dimensional Location. 5. Algorithms. 6. Outlier Diagnostics. 7. Related Statistical Techniques. References. Table of Data Sets. Index.

Robust Regression and Outlier Detection

Robust Regression and Outlier Detection: Rousseeuw/Robust Regression & Outlier Detection

Wiley Series in Probability and Mathematical Statistics

relationships by maximum likelihood and least squares methods. Uppsala, Sweden: University of Uppsala. Muth6n, B. (1987). LISCOMP: Software for advanced analysis of L.S.E. with a comprehensive measurement model. Mooresville, IN: Scientific Software. Satorra, A., & Bentler, P. M. (1988). Model conditions for asymptotic robustness in the analysis of linear relations (UCLA Statistics Series #4). Los Angeles: University of California. Shapiro, A. (1986). Asymptotic theory for overparameterized structural models. Journal of the American Statistical Association, 81, 142-149.

http://www.jstor.org/stable/pdfplus/1164710.pdf

Annick M. Leroy

Papers

Robust Regression and Outlier Detection

Robust Regression and Outlier Detection: Rousseeuw/Robust Regression & Outlier Detection

Wiley Series in Probability and Mathematical Statistics

Robust Regression & Outlier Detection

Least median of squares: a robust method for outlier and model error detection in regression and calibration