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

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.

/pdf/distributed-optimization-and-statistical-learning-via-the-8s9gtddw0d.pdf

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and implementation of the WordNet lexical database and searching software, Randee I. Tengi. Part 2: automated discovery of WordNet relations, Marti A. Hearst representing verb alterations in WordNet, Karen T. Kohl et al the formalization of WordNet by methods of relational concept analysis, Uta E. Priss. Part 3 Applications of WordNet: building semantic concordances, Shari Landes et al performance and confidence in a semantic annotation task, Christiane Fellbaum et al WordNet and class-based probabilities, Philip Resnik combining local context and WordNet similarity for word sense identification, Claudia Leacock and Martin Chodorow using WordNet for text retrieval, Ellen M. Voorhees lexical chains as representations of context for the detection and correction of malapropisms, Graeme Hirst and David St-Onge temporal indexing through lexical chaining, Reem Al-Halimi and Rick Kazman COLOR-X - using knowledge from WordNet for conceptual modelling, J.F.M. Burg and R.P. van de Riet knowledge processing on an extended WordNet, Sanda M. Harabagiu and Dan I Moldovan appendix - obtaining and using WordNet.

WordNet : an electronic lexical database

https://ai.stanford.edu/~ronnyk/wrappersPrint.pdf

Wrappers for feature subset selection

This talk covers techniques for analyzing data sets with up to trillions of examples with billions of features, using thousands of computers. To operate at this scale requires an understanding of an increasing complex hardware hierarchy (e.g. cache, memory, SSD, another machine in the rack, disk, a machine in another data center, ...); a model for recovering from inevitable hardware and software failures; a machine learning model that allows for efficient computation over large, continuously updated data sets; and a way to visualize and share the results.

Internet scale data analysis

If you want to recognize speech or filter out spam emails, you will probably write a machine learning algorithm and will not try to write the whole program using a "traditional" software specification and implementation. There are many examples of successful machine learning solutions, but can we more broadly apply the techniques to most or all software problems, and for most or all programmers, from the novice in their first programming course to the seasoned professional?

Machine learning for programming

Considering that much contemporary natural science involves field expeditions, fieldwork is an under-studied topic. There is also little information technology specifically designed to support scientific fieldwork, aside from portable scientific instruments. This article describes a variety of fieldwork practices in an interdisciplinary research area, proposes a framework linking types of fieldwork to types of needs in information technology, and identifies promising opportunities for technology development. Technologies that are designed to support the integration of field observations and samples with laboratory work are likely to aid nearly all research teams who conduct fieldwork. However, technologies that support highly detailed representations of field sites will likely trigger the deepest changes in work practice. By way of illustration, we present brief case studies of how fieldwork is done today and how it might be conducted with the introduction of new information technologies.

https://ntrs.nasa.gov/api/citations/20020038886/downloads/20020038886.pdf

Science in the Wild: Technology Needs and Opportunities in Scientific Fieldwork

There have been several recent articles in the SIGART Newsletter about the game of Mastermind (T. M. Rao, #82, E. Shapiro, #85, P. Koppstein, #88). This note describes an algorithm that is different from the others in that it is specifically designed to be an approximation to the optimal strategy.

Playing Mastermind optimally

When using model checking to verify programs in practice, it is not usually possible to achieve complete coverage of the system. In this position paper we describe ongoing research within the Automated Software Engineering group at NASA Ames on the use of test coverage metrics to measure partial coverage and provide heuristic guidance for program model checking. We are specifically interested in applying and developing coverage metrics for concurrent programs that might be used to support certification of next generation avionics software.

/pdf/coverage-metrics-for-model-checking-3ex5cwh7lz.pdf

Peter Norvig

Papers

Internet scale data analysis

Machine learning for programming

Science in the Wild: Technology Needs and Opportunities in Scientific Fieldwork

Playing Mastermind optimally

Coverage Metrics for Model Checking