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

In water resources risk management, since water dispatching decision at the later stage lacks sufficient support, because of low accuracy, in forecasting, a combined forecasting method is put forward. The combined forecasting method constructs an angle cosine model through measurement vector, prediction vector and weight vector; upon determining variant quotient, water resources are rated “sufficient” or “insufficient” taking into account historical water resources and collected statistics, and calibration and initialization of parameters are determined by fitting goodness and dynamic approximation, switching the forecast from a solo to dual model, meanwhile comparing implications of prediction accuracies which assure the forecast supports the coordination. As the experiment demonstrates, this methodology can effectively control risks and shortcomings of a solo model of water resources risk management. Through merging various forecasting models, forecasting accuracy is improved by 20%, lowering the risk of water dispatching decisions while providing referable statistics applied in the water resources risk management.

Combined Forecasting Method Based on Water Resources Risk Management

On social networking sites, people can express themselves in a variety of ways such as creating personalized profiles, commenting on some topics, sharing their experiences and thoughts. Among these technology-enabled features, retweeting other-sourced tweet is a powerful way for users to present themselves. We examine users’ retweeting behavior from the perspective of online identity and self-presentation. The empirical results based on a panel dataset crawled from Twitter reveal that, people are prone to retweet topics they are interested in and familiar with, in order to convey a consistent and clear online identity. In addition, we also examine which user groups exhibit a stronger propensity for a clear online identity, considering the practical value of these users to both social media platforms and marketers. By integrating self-presentation theory with social influence theory and social cognitive theory, we propose and confirm that users with higher value in online self-presentation efficacy and users who are more involved with the social media platform have a stronger than average propensity to maintain a consistent online identity, and thus are more likely to retweet familiar topics. These users are characterized by (1) owning a larger number of followers, (2) authoring longer and more original tweets than average, (3) being active in retweeting other-sourced posts. This study contributes to our understanding of SNS users’ retweeting behavior and adds to the emerging line of research on online identity. It also provides insights on how microblogging service providers and enterprises can promote people’s retweeting behavior.

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Examining retweeting behavior on social networking sites from the perspective of self-presentation

Present-day enterprise accounting solutions have been developed to a certain extent to provide authenticity of accounting information and to provide modules for billing, pay role, general ledger, and more, but they come with certain problems such as distortion of accounting information, incomplete selection of indicator variables, and the limited and single use of identification methods. Based on this, this study starts with two points. The first is to give the concepts of decision trees and support vector machine (SVM) in data mining. Then, the accounting distortion information identification model is constructed based on this, and the model effect is verified by setting experiments. The second is to establish a regression model on the relationship between enterprise strategy and accounting information quality to further explore the factors that affect the quality of enterprise accounting information. The following are the research results: (1) The accuracy rates of classification and identification of training set data, overall data, and test set data using the SVM-based identification model are 99.19%, 96.21%, and 94.8%, respectively. (2) The average identification rate of the sample data is 88.5% using the identification model based on the decision tree. (3) The regression coefficients of enterprise strategy and accounting information quality are −0.053 and −0.054, respectively without considering the industry and year variables and with considering the industry and year variables, both of which are negative at the 0.1 significance level. The purpose of this study is to use data mining to achieve high-quality identification of enterprise accounting information and provide some references for enterprises to choose or formulate relevant development strategies.

https://downloads.hindawi.com/journals/misy/2022/7668276.pdf

Enterprise Accounting Information Identification and Strategic Management under Data Mining Technology

Researches on giving, charity switching behaviour and donors' loyalty can easily be found in a half dozen American states or regions. However, there is still a gap between non-western countries on similar researches in terms of scope and depth. With such deficiency, this study attempts to make use of the case in Hong Kong in order to fill the research gap by developing localized tools to understand the factors impacting the donation switching behaviour which can help the Non-Governmental Organizations (NGOs) to develop fundraising strategies that fit the local needs. This study shows that NGOs should keep their organization unique with ongoing updates to/from donors. Also, they should not solely rely on traditional classification factors like donation amount to identify loyalty donors.

A Study on the Switching Pattern and Donors' Loyalty for Charitable Behavior in Hong Kong

There exist inclination and selection processes in enterprises' front collaboration based on Agile Virtual Enterprises (AVE) and Collaborative Planning, Forecasting and Replenishment (CPFR) mechanisms. This paper analyzes the existing configurations and dynamic features of AVE collaborations; and it also explicates enterprises' management patterns that have shifted from vertical to horizontal integration and have displayed AVE characteristics, corresponding to CPFR mechanism and its front collaborative protocol management. It further points out that AVE front collaborative relationship consists of such factors as impact, intimacy and vision, particularly displaying AVE collaborative protocol and features. Based on the motivation for constructing front collaboration and conditional models for AVE-based CPFR mechanisms, this paper addresses four issues: (i) the desire for maximum returns for AVE members is the driving force for the formation and growth of AVE; (ii) AVE can improve resources' utilization effectively; (iii) inclination and selection processes play an important role when AVE's collaborative protocols are formulated; and (iv) capacity and efficiency are preconditions for enterprises to become AVE members.

Kin Keung Lai

Papers

Combined Forecasting Method Based on Water Resources Risk Management

Examining retweeting behavior on social networking sites from the perspective of self-presentation

Enterprise Accounting Information Identification and Strategic Management under Data Mining Technology

A Study on the Switching Pattern and Donors' Loyalty for Charitable Behavior in Hong Kong

An Analysis of Motivation for Front Collaboration of AVE-based CPFR Mechanisms