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

MBT : Memory Based Tagger, version 1.0, Reference Guide

The style in which a text is written re ects an array of meta-information concerning the text (e.g., topic, register, genre) and its author (e.g., gender, region, age, personality). The eld of stylometry addresses these aspects of style. A successful methodology, borrowed from text categorisation research, takes a two-stage approach which (i) achieves automatic selection of features with high predictive value for the categories to be learned, and (ii) uses machine learning algorithms to learn to categorize new documents by using the selected features (Sebastiani, 2002). To allow the selection of linguistic features rather than (n-grams of) terms, robust and accurate text analysis tools are necessary. Recently, language technology has progressed to a state of the art in which the systematic study of the variation of these linguistic properties in texts by different authors, time periods, regiolects, genres, registers, or even genders has become feasible.

Using syntactic features to predict author personality from text

This paper describes a new method, Combi-bootstrap, to exploit existing taggers and lexical resources for the annotation of corpora with new tagsets. Combi-bootstrap uses existing resources as features for a second level machine learning module, that is trained to make the mapping to the new tagset on a very small sample of annotated corpus material. Experiments show that Combi-bootstrap: i) can integrate a wide variety of existing resources, and ii) achieves much higher accuracy (up to 44.7 % error reduction) than both the best single tagger and an ensemble tagger constructed out of the same small training sample.

Bootstrapping a Tagged Corpus through Combination of Existing Heterogeneous Taggers

We propose to use data mining techniques (inductive techniques for the automatic acquisition of comprehensibl e knowledge from data) as a method in linguistic analysis. In the past, such techniques have mainly been used in linguistic engineering applications to solve knowledge acquisition bottlenecks. In this paper we show that they can also assist in linguistic theory formation by providing a new tool for the evaluation of linguistic hypotheses, for the extraction of rules from corpora, and for the discovery of useful linguistic categories. By applying a rule induction method to a particular linguistic task (diminutive formation in Dutch) we show that data mining techniques can be used to test linguistic hypotheses about this morphological proces, and to discover interesting morphological and phonological rules and categories.

Data Mining as a Method for Linguistic Analysis: Dutch Diminutives

In this article, we describe a novel approach to sequence tagging for languages that are rich in (e.g. orthographic) surface variation. We focus on lemmatization, a basic step in many processing pipelines in the Digital Humanities. While this task has long been considered solved for modern languages such as English, there exist many (e.g. historic) languages for which the problem is harder to solve, due to a lack of resources and unstable orthography. Our approach is based on recent advances in the field of ‘deep’ representation learning, where neural networks have led to a dramatic increase in performance across several domains. The proposed system combines two approaches: on the one hand, we apply temporal convolutions to model the orthography of input words at the character level; secondly, we use distributional word embeddings to represent the lexical context surrounding the input words. We demonstrate how this system reaches state-of-the-art performance on a number of representative Middle Dutch data sets, even without corpus-specific parameter tuning.

Walter Daelemans

Papers

MBT : Memory Based Tagger, version 1.0, Reference Guide

Using syntactic features to predict author personality from text

Bootstrapping a Tagged Corpus through Combination of Existing Heterogeneous Taggers

Data Mining as a Method for Linguistic Analysis: Dutch Diminutives

Lemmatization for variation-rich languages using deep learning