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.

Data Mining Practical Machine Learning Tools and Techniques

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

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Machine learning in automated text categorization

In a traditional library system, a search result will be exactly the same if different users with different preferences use the same search criteria. It is obvious that the traditional library system cannot provide high QoS (Quality of Service) for different users. To solve this problem, a personalized library search agent technique is proposed based on data mining technology. By mining the training data sets, the attributes that are related to borrowing tendency of users are analyzed, and then users are divided into different groups. The SLIQ (Supervised Learning In Quest) algorithm is used to mine data in this Web-based personalized library search agent system successfully. Simulations have shown the personalized library search agent can generate personalized search results based on users’ preferences and usage. Therefore, a user can use the personalized library search agent to get quick result. The fuzzy Web search agent and the granular Web search agent are proposed to deal with uncertainty and complexity of huge amounts of Web data. In general, Computational Web Intelligence (CWI) can be used in the personalized search agent to improve QoS for Web users.

Personalized Search Agents Using Data Mining and Granular Fuzzy Techniques

Shenoy's valuation based system provides a knowledge representation framework for a variety of applications. In this paper, we study the valuation based systems using the notions developed for relational databases. It is shown that the objects and operations in a valuation based system can in fact be conveniently expressed using relational database terminology. In this framework, the construction of a suitable Markov tree, for example, can be viewed as query optimization. This study explains some of the assumptions inherent in such a query processing, which can lead to an efficient implementation of valuation based systems.

Towards Implementing Valuation Based Systems with Relational Databases

Creating an adaptive web site, which is a web site that automatically changes its contents and organization according to usage, is a challenge for web site designers. We introduce a novel algorithm for creating an adaptive web site based on combining web usage mining and adaptive web site generation. We outline the design of a system called WebAdaptor that implements our ideas. We elaborate on the usage mining module, adaptation module, and page generation module in the WebAdaptor system.

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WebAdaptor: Designing Adaptive Web Sites Using Data Mining Techniques

This paper extends the former study of reduct construction algorithms by taking an intensive study on two complementary control strategies: the deletion methods and the addition methods. Based on their differences and properties, they can be used to construct different kinds of reducts. The winning reducts of the left-to-right lexical order and the right- to-left lexical order can be constructed by the addition methods and the deletion methods, respectively.

Deletion and Addition Methods for Reduct Construction

Three-way decision is about thinking, problem-solving, and computing in threes or through triads. By dividing a whole into three parts, by focusing on only three things, or by considering three basic ingredients, we may build a theory, a model, or a method that is simple-to-understand, easy-to-remember, and practical-to-use. This philosophy and practice of triadic thinking appears everywhere. In particular, there are a number of three-world or tri-world models in different fields and disciplines, where a complex system, a complicated issue, or an intricate concept is explained and understood in terms of three interrelated worlds, with each world enclosing a group of elements or representing a particular view. The main objective of this paper is to review and re-interpret various three-world conceptions through the lens of three-way decision. Three-world conceptions offer more insights into three-way decision with new viewpoints, methods, and modes. They can be used to construct easy-to-understand explanations in explainable artificial intelligence (XAI). 

Yiyu Yao

Papers

Personalized Search Agents Using Data Mining and Granular Fuzzy Techniques

Towards Implementing Valuation Based Systems with Relational Databases

WebAdaptor: Designing Adaptive Web Sites Using Data Mining Techniques

Deletion and Addition Methods for Reduct Construction

Three-way Decision, Three-World Conception, and Explainable AI