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

Ramping tricks of trade-based stock manipulation have evolved greatly in the fight with stricter market regulation, and can be extremely complicated nowadays. Despite the rigidity and soundness, theoretical models proposed in extant literature can hardly be applied directly to real market data, due to their assumptions being far away from reality. On the other hand, empirical studies of ramping manipulation still lack guidance and support from theories that can better reflect ramping details in practice. This paper addresses this gap by constructing a theoretical model that is closely linked to practical detection, in the framework of behavioral finance. New insights into concrete ramping manipulation tricks are also contributed to the literature. The potential of the model for manipulation detection is demonstrated by applying it to the two most infamous manipulation cases in the history of Chinese stock market.

https://link.springer.com/content/pdf/10.1007%2Fs10614-013-9412-9.pdf

A Model of Stock Manipulation Ramping Tricks

This article considers when preventive maintenance (PM) and replacement should be implemented for a repairable series system with failure interaction. Under the main assumption that the system gradually deteriorates with time, the modified sequential method is proposed to determine an optimal PM and replacement policy for the system based on minimizing expected cost per unit operation time. According to this method, PM epochs are determined one after the other, and consequently we can make use of all previous information on the system's operation process. Also, the replacement epoch depends on the PM's effective length. A property of the cost function with respect to both PM and replacement is discussed.

A preventive maintenance and replacement policy of a series system with failure interaction

In this study, a four-stage SVM-based multiagent ensemble learning approach is proposed for group decision making problem. In the first stage, the initial dataset is divided into training subset and testing subset for training and testing purpose. In the second stage, different SVM learning paradigms with much dissimilarity are constructed as diverse agents for group decision making. In the third stage, multiple single SVM agents are trained using training subset and the corresponding decision resulcts are also obtained. In the final stage, all individual results produced by multiple single SVM agents are aggregated into a group decision result. Particularly, the effects of different diversity strategies and different ensemble strategies on multiagent ensemble learning system are tested. For illustration, one credit application approval dataset is used and empirical results demonstrated the impacts of different diversity strategies and ensemble strategies.

Testing of Diversity Strategy and Ensemble Strategy in SVM-Based Multiagent Ensemble Learning

We establish some characterizations of the solution set of nonsmooth pseudoinvex minimization problems where the function involved is locally Lipschitz and Clarke differentiable. Our results extend and unify several results from the literature to nonsmooth case.

On Characterization of Solution Sets of Nonsmooth Pseudoinvex Minimization Problems

Allocation of emergency mobile facility is the key problem of the emergency response system, which affects the cost and the satisfaction of services for emergency so as to rapidly respond to disasters, contagions, etc. In order to seek a reasonable fleet size and locations of emergency mobile facilities, we propose a two-stage programming model with the objective function of minimizing the total cost. With uncertain characteristics of emergency event, the model conforms to the requirement for noninterruptible service and tries to satisfy all emergency demands. For overcoming the computational difficulty of emergency mobile facility fleet size and location problem, a robust optimization approach and modified ant colony algorithm are employed to obtain solutions. The illustrative example shows that the model can provide a reasonable solution to the determination of the fleet size and locations of emergency mobile facilities and that the risk recognition factor of the model can further guide decision-making.

/pdf/robust-optimization-solution-to-emergency-mobile-facility-4ny1dar228.pdf

Kin Keung Lai

Papers

A Model of Stock Manipulation Ramping Tricks

A preventive maintenance and replacement policy of a series system with failure interaction

Testing of Diversity Strategy and Ensemble Strategy in SVM-Based Multiagent Ensemble Learning

On Characterization of Solution Sets of Nonsmooth Pseudoinvex Minimization Problems

Robust Optimization Solution to Emergency Mobile Facility Fleet Size and Location