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

To perform diagnosis and treatment, health systems, hospitals, and other patient care facilities require a wide range of supplies, from masks and gloves to catheters and implants. The “healthcare supply chain/healthcare operation management” refers to the stakeholders, systems, and processes required to move products from the manufacturer to the patient’s bedside. The ultimate goal of the healthcare supply chain is to ensure that the right products, in the right quantities, are available in the right places at the right time to support patient care. Hospitals and the concept of a healthcare delivery system are practically synonymous. Surgical services, emergency and disaster services, and inpatient care are the three main types of services they offer. Outpatient clinics and facilities are also available at some hospitals, where patients can receive specialty consultations and surgical services. There will always be a need for inpatient care, regardless of how care models develop. The focus of this monograph was on recent OM work that models the dynamic, interrelated effects of demand-supply matching in the ED, OR, and inpatient units. Decisions about staffing and scheduling in these areas are frequently made independently by healthcare managers and clinicians. Then, as demand changes in real-time, clinicians and managers retaliate as best as they can to reallocate staffing to the areas that require it most at a particular moment in time in order to relieve patient flow bottlenecks. We, as OM researchers, must create models that help healthcare administrators enhance OR scheduling policies, ED demand forecasting, and medium- and short-term staffing plans that consider the interdependence of how demand develops.

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A Review of Emergency and Disaster Management in the Process of Healthcare Operation Management for Improving Hospital Surgical Intake Capacity

A method for winner determination under sealed-bids auction based on a new measurement of the bid criteria

We consider price competition with a linear demand function and compare three scenarios. To compare the impact of revenue-sharing or wholesale price contracts on manufacturers, retailers and supply chains. In the three common cases, the maximum profit of retailers, manufacturers and supply chains was analyzed and compared. We consider the interaction between the various links of the supply chain, and through different strategies, achieve the optimal decision of wholesale price and order quantity. By comparing the profit trends between manufacturers, retailers and supply chains, and the difference in profits, we can get different strategies for the supply chain in the two competing supply chains. And the impact of the profits of retailers and manufacturers is significant.

Revenue-Sharing Versus Wholesale Price Contracts Under Chain-to-Chain Competition

Analysis of supply chain risk and cost with simulation methods

In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection.

Kin Keung Lai

Papers

A Review of Emergency and Disaster Management in the Process of Healthcare Operation Management for Improving Hospital Surgical Intake Capacity

A method for winner determination under sealed-bids auction based on a new measurement of the bid criteria

Revenue-Sharing Versus Wholesale Price Contracts Under Chain-to-Chain Competition

Analysis of supply chain risk and cost with simulation methods

Self-Organizing-Map-Based Metamodeling for Massive Text Data Exploration