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

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

Since Thomason's paper in 1977 showing that the max-min powers of a fuzzy matrix either converge or oscillate with a finite period, many different algebraic operations are employed to explore the limiting behavior of powers of a fuzzy matrix, such as max-min/max-product/max-Archimedean t-norm/max-t-norm/max-arithmetic mean operations. In this article, we consider the max-quasi-arithmetic mean powers of a fuzzy matrix which is an extensive case of the max-arithmetic mean, max-root power mean and max-convex mean. We also show that the powers of such fuzzy matrices are always convergent. Some numerical examples are provided to show the situation of convergence.

On the Max-quasi-Arithmetic Mean Powers of a Fuzzy Matrix

This paper considers the credit authorization problem in credit card companies’ authorization systems. Customers are classified into several risk segments according to the type of services requested and the associated credit risk of customers. Card companies need to balance the risk exposure and the quality of customer service in the decision of referral/no referral. We formulate this problem as a Markov Decision Process (MDP) and characterize the optimal policy as referral limit control for each risk segment. A numerical example is provided to verify our results.

Referral Limit Policy for the Credit Authorization Process

As a widely used medium platform, Micro-blog influence research is a hotspot. The community micro-blog, which is used as an effective tool by social managers in virtual community, has developed rapidly in recent years. As the basis of government micro-blog system in China, the community blog influence has great importance to guide the public popular feelings and guarantee the safety of the virtual social network. The article focuses on the evaluation methods of the community micro-blog influence. Firstly, an index system is presented, both quantitative index and qualitative index are considered based on the mechanism of information dissemination of micro-blog, Then, principal component analysis (PCA) is used to compose these indexes into some comprehensive indexes to simplify the index system, finally, support vector machine (SVM) is adopted for the evaluation model. Practical examples show that the model established in this paper outperforms others in evaluation accuracy.

Evaluation Model Based on Support Vector Machine for Community Micro-Blog Influence

In order to investigate the efficiency of policies made by the industrial administration office (IAO for short) so as to keep market order, the authors propose a shocked inventory model of one product with both genuine and counterfeit qualities. The checking policies made by the IAO are considered to be a random shock for the inventory model by examination of the qualities of the products, which will expropriate the counterfeit products. The paper makes two contributions. First, while discussing several conditions, the authors provide a basic theory, which is a kind of extension of the newsvendor problem, and prove some properties of the optimal policies for the shocked inventory model with a fixed random checking rate. Second, the authors give some recommendations as to how the IAO can formulate its checking policies. An example demonstrates the relationship between checking policies and optimal inventory policies. � 2003 Elsevier B.V. All rights reserved.

Production, Manufacturing and Logistics Single period, single product newsvendor model with random supply shock

In this study, we propose a business intelligent model integrating econometric models, i.e. ARMA, GARCH, and ANN models for VaR estimation. The business intelligent model achieves better efficiency in input variables selecting because they are selected and newly created by time series models. Repetitive trial error process could be effectively eliminated to one time series process. On the other hand, the performance of traditional time series models could be further enhanced by the forecasting power of ANN models. Empirical study shows that the business intelligent model can improve the predictive power in the framework of both accuracy and reliability.

Kin Keung Lai

Papers

On the Max-quasi-Arithmetic Mean Powers of a Fuzzy Matrix

Referral Limit Policy for the Credit Authorization Process

Evaluation Model Based on Support Vector Machine for Community Micro-Blog Influence

Production, Manufacturing and Logistics Single period, single product newsvendor model with random supply shock

A Business Intelligent Model for Market Risk Measurement