Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Statistical approaches to processing natural language text have become dominant in recent years This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear The book contains all the theory and algorithms needed for building NLP tools It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications

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Foundations of Statistical Natural Language Processing

Data Mining Practical Machine Learning Tools and Techniques

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques.

In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.

Contact: yvan.saeys@psb.ugent.be

Supplementary information: http://bioinformatics.psb.ugent.be/supplementary_data/yvsae/fsreview

A review of feature selection techniques in bioinformatics

Starting from the distributional bootstrapping hypothesis, we propose an unsupervised model that selects the most useful distributional information according to its salience in the input, incorporating psycholinguistic evidence. With a supervised Parts-of-Speech tagging experiment, we provide preliminary results suggesting that the distributional contexts extracted by our model yield similar performances as compared to current approaches from the literature, with a gain in psychological plausibility. We also introduce a more principled way to evaluate the effectiveness of distributional contexts in helping learners to group words in syntactic categories.

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Which distributional cues help the most? Unsupervised contexts selection for lexical category acquisition

Effective information systems require the existence of explicit process models; a completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and the resulting models are often subjective and incomplete. We propose a method that discovers the process model from process logs where process events are recorded as they have been executed over time. We induce rules that predict causal, exclusive, and parallel relations between process events. These relation information are used by an already developed α-algorithm to construct a process model in the Petri-net formalism. The rules are induced from simulated process log data that are generated by varying process characteristics (e.g. noise, log size). Tests reveal that the induced rules has a high performance on new data.

/pdf/discovering-process-models-by-rule-set-induction-1248p01nhc.pdf

Discovering process models by rule set induction

BENELEARN-97: proceedings of the seventh Belgian-Dutch Conference on Machine Learning

Invited Speaker Gregory Grefenstette: Very Large Lexicons Regular Papers Julie Carson-Berndsen, Gina Joue and Michael Walsh: Phonotactic Constraint Ranking for Speech Recognition Lars Borin and Klas Prutz: Through a glass darkly: Part-of-speech distribution in original and translated text Gosse Bouma, Gertjan van Noord, and Robert Malouf: Alpino: Wide-coverage Computational Analysis of Dutch Pius ten Hacken: Revolution in Computational Linguistics: Towards a Genuinely Applied Science Heleen Hoekstra, Michael Moortgat, Ineke Schuurman, and Ton van der Wouden: Syntactic Annotation for the Spoken Dutch Corpus Project (CGN) Andre Kempe: Part-of-Speech Tagging with Two Sequential Transducers Wessel Kraaij and Renee Pohlmann: Different approaches to Cross Language Information Retrieval S. Marcus, C. Martin-Vide, V. Mitrana, Gh. Paun: A New-Old Class of Linguistically Motivated Regulated Grammars Michael Moortgat and Richard Moot: CGN to Grail: Extracting a Type-logical Lexicon from the CGN Annotation Thierry Poibeau and Leila Kosseim: Proper Name Extraction from Non-Journalistic Texts Ielka van der Sluis and Emiel Krahmer: Generating Referring Expressions in a Multimodal Context: An empirically oriented approach Erik F. Tjong Kirn Sang: Transforming a Chunker to a Parser Antal van den Bosch, Emiel Krahmer and Marc Swerts: Automatic Detection of Problematic Turns in Human-Machine Interactions List of Contributors

Walter Daelemans

Papers

Which distributional cues help the most? Unsupervised contexts selection for lexical category acquisition

Discovering process models by rule set induction

BENELEARN-97: proceedings of the seventh Belgian-Dutch Conference on Machine Learning

Computational linguistics in the Netherlands 2000 : selected papers from the eleventh CLIN Meeting

Phoneme-to-grapheme conversion for out-of-vocabulary items in speech recognition