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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Does the reason a user visits a Wikipedia page influence that user's subsequent browsing behavior on Wikipedia? We address this question using aggregate Wikipedia page access data. We conduct: (1) a comparison of browsing behaviors between serendipitous and directed information seekers; and (2) how topic/category influences users' migration from page to page. Our findings indicate that surfer behavior and topic are potentially influential factors, which weakens the random surfer model underlying link-based algorithms, and could affect how web designers should design sites to best meet the diversity of information seeking behaviors.

Surfin' Wikipedia: an analysis of the Wikipedia (non-random) surfer's behavior from aggregate access data

In the present article we introduce and validate an approach for single-label multi-class document categorization based on text content features. The introduced approach uses the statistical property of Principal Component Analysis, which minimizes the reconstruction error of the training documents used to compute a low-rank category transformation matrix. Such matrix transforms the original set of training documents from a given category to a new low-rank space and then optimally reconstructs them to the original space with a minimum reconstruction error. The proposed method, called Minimizer of the Reconstruction Error (mRE) classifier, uses this property, and extends and applies it to new unseen test documents. Several experiments on four multi-class datasets for text categorization are conducted in order to test the stable and generally better performance of the proposed approach in comparison with other popular classification methods.

Minimizer of the Reconstruction Error for multi-class document categorization

The TadPolemic system identifies whether web search queries 1 are controversial in nature and/or 2 pertain to children's topics. We are incorporating it into a children's web search engine to assist children's search during difficult topics, as well as to provide filtering or mitigation of bias in results when children search for contentious topics. We show through an evaluation that the system is effective at detecting kids' topics and controversies for a broad range of topics. Though designed to assist children, we believe these methods are generalizable beyond young audiences and can be usefully applied in other contexts.

Clash of the Typings

In this work we present and evaluate a methodology to classify web documents into a predefined hierarchy using the textual content of the documents. The general problem of hierarchical classification using taxonomies with thousands of categories is a hard task due to the problem of scarcity of training data. Hierarchical classification is one of the rare situations where, despite the large amount of available data, as more documents become available, more classes are also added to the hierarchy. This leads to a lack of training data for most of the categories, which produces poor individual classification models and tends to bias the classification to dense categories. Here we propose a novel feature extraction technique called Stratified Discriminant Analysis (sDA) that reduces the dimensions of the text-content features of the web documents along the different levels of the hierarchy. The sDA model is intended to reduce the effects of scarcity of data by better grouping and identify the categories with few training examples leading to more robust classification models for those categories. The results of classifying web pages from the Kids&Teens branch of the DMOZ directory show that our model extracts features that are well suited for category grouping of web pages and representation of categories with few training examples.

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Hierarchical classification of web documents by stratified discriminant analysis

Multimodal question answering in the cultural heritage domain allows visitors to ask questions in a more natural way and thus provides better user experiences with cultural objects while visiting a museum, landmark or any other historical site. In this paper, we introduce the construction of a golden standard dataset that will aid research of multimodal question answering in the cultural heritage domain. The dataset, which will be soon released to the public, contains multimodal content including images of typical artworks from the fascinating old-Egyptian Amarna period, related image-containing documents of the artworks and over 800 multimodal queries integrating visual and textual questions. The multimodal questions and related documents are all in English. The multimodal questions are linked to relevant paragraphs in the related documents that contain the answer to the multimodal query.

Marie-Francine Moens

Papers

Surfin' Wikipedia: an analysis of the Wikipedia (non-random) surfer's behavior from aggregate access data

Minimizer of the Reconstruction Error for multi-class document categorization

Clash of the Typings

Hierarchical classification of web documents by stratified discriminant analysis

A dataset for multimodal question answering in the cultural heritage domain