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

Part I: AN INTRODUCTION TO INQUIRY. 1. Human Inquiry and Science. 2. Paradigms, Theory, and Research. 3. The Ethics and Politics of Social Research. Part II: THE STRUCTURING OF INQUIRY: QUANTITATIVE AND QUALITATIVE. 4. Research Design. 5. Conceptualization, Operationalization, and Measurement. 6. Indexes, Scales, and Typologies. 7. The Logic of Sampling. Part III: MODES OF OBSERVATION: QUANTITATIVE AND QUALITATIVE. 8. Experiments. 9. Survey Research. 10. Qualitative Field Research. 11. Unobtrusive Research. 12. Evaluation Research. Part IV: ANALYSIS OF DATA:QUANTITATIVE AND QUALITATIVE . 13. Qualitative Data Analysis. 14. Quantitative Data Analysis. 15. Reading and Writing Social Research. Appendix A. Using the Library. Appendix B. Random Numbers. Appendix C. Distribution of Chi Square. Appendix D. Normal Curve Areas. Appendix E. Estimated Sampling Error.

社会研究方法基础 = The Basics of Social Research

Open Innovation: The New Imperative for Creating and Profiting from Technology

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

Understanding the small business sector

R&D as a determinant of innovation in manufacturing SMEs: An attempt at empirical clarification

Purpose – The purpose of this paper is to shed light on the nature of intellectual capital in small to medium‐sized enterprises (SMEs) and how it is linked to strategy and performance.Design/methodology/approach – Using structural equations, a multivariate model is presented where multiple relations are tested between different components of intellectual capital and performance. The model is tested first on a unique sample of 267 SMEs and second on two subsamples where SMEs are grouped according to their strategic profile.Findings – Findings confirm that SMEs that adopt different strategies organize their intellectual capital in a particular and adapted way. When an attempt is made to link intellectual capital components to performance, it is noticed that the latter is strategy specific, just as the variables that influence performance. Prospectors dominate defenders on most intellectual capital components.Research limitations/implications – Use of secondary data may provide less precise results that coul...

Intangible assets and performance

Purpose – In order to deepen one's knowledge and further build theory on the implementation and use of advanced manufacturing systems (AMS) in small and medium‐sized enterprises (SMEs), the present research seeks to explore the following questions: What is the present level of AMS sophistication in SMEs? What characteristics of the SMEs' strategic, organizational and entrepreneurial context are associated with higher levels of AMS sophistication? And what are the operational and business performance impacts of this sophistication for small and medium‐sized manufacturers?Design/methodology/approach – A survey of 248 Canadian manufacturers was used to collect data that were analyzed by structured equation modeling.Findings – AMS sophistication significantly impacts both the operational performance and the business performance of SMEs. Antecedents of this sophistication include the education and experience of the owner‐manager, the strategic orientation of the firm, the type of production, and the commercial...

Antecedents and performance outcomes of advanced manufacturing systems sophistication in SMEs

Purpose – The purpose of this paper is to show that benchmarking allows SMEs to improve their operational performance.Design/methodology/approach – The paper presents a fully implemented expert diagnostic system which evaluates on a benchmarking basis the performance of SMEs.Findings – The research results with hundreds of SMEs show that benchmarking allows them to improve their operational and financial performance thus confirming the usefulness of benchmarking for SMEs, especially since traditional performance models for large enterprises do not apply well to SMEs.Research limitations/implications – Based on data mining techniques, future work should allow us to significantly extend our knowledge on SMEs, and further improve our evaluation model of SME performance.Practical implications – Practitioners and researchers should pay more attention to benchmarking as a valuable performance evaluation tool, not only for large businesses, but for SMEs as well.Originality/value – The paper highlights the develo...

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An expert diagnosis system for the benchmarking of SMEs' performance

With the advent of globalization and the knowledge economy, an important issue lies in the strategic capabilities that enable the internationalization of SMEs. Using a configurational approach grou...

Josée St-Pierre

Papers

R&D as a determinant of innovation in manufacturing SMEs: An attempt at empirical clarification

Intangible assets and performance

Antecedents and performance outcomes of advanced manufacturing systems sophistication in SMEs

An expert diagnosis system for the benchmarking of SMEs' performance

Strategic capability configurations for the internationalization of SMEs: A study in equifinality: