The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods. Adaptive Computation and Machine Learning series

Introduction to Machine Learning

行銷硏究 : Marketing research

Hedonic shopping motivations

This meta-analysis explored the efficacy of the Theory of Planned Behaviour (TPB) dependent on behaviour and methodological moderators. A lack of hierarchical analysis in previous reviews risks confounding these moderators. Here moderating roles of behaviour type, length of follow-up, sample age and behavioural measure are explored hierarchically amongst prospective tests of the TPB, controlling for past behaviour where possible. Searching identified 237 prospective tests from 206 articles. Random-effects meta-analytic procedures were used to correcting correlations for sampling and measurement error. Behaviour type moderated the model; physical activity and diet behaviours were better predicted (23.9% and 21.2% variance explained, respectively) whilst risk, detection, safer sex and abstinence from drugs were poorly predicted (between 13.8 and 15.3% variance explained). Methodological moderators were also apparent: age of sample moderated relations with student samples better predicted for physical activi...

Prospective prediction of health-related behaviours with the Theory of Planned Behaviour: a meta-analysis

Organizational innovation adoption: a multi-level framework of determinants and opportunities for future research

Purpose – To provide a thorough analysis of the role of the internet in survey research and to discuss the implications of online surveys becoming such a major force in research.Design/methodology/approach – The paper is divided into four major sections: an analysis of the strengths and potential weaknesses of online surveys; a comparison of online surveys with other survey formats; a discussion on the best uses for online surveys and how their potential weaknesses may be moderated; and an overview of the online survey services being offered by the world's largest research firms.Findings – If conducted properly, online surveys have significant advantages over other formats. However, it is imperative that the potential weaknesses of online surveys be mitigated and that online surveys only be used when appropriate. Outsourcing of online survey functions is growing in popularity.Practical implications – The paper provides a very useful source of information and impartial advice for any professional who is co...

The value of online surveys

This article presents a large-scale cross-sectional field study of the effect of store environment on consumer emotions and the resulting influence on aspects of consumer behavior with actual shopping behavior used as an example. Cast into a stimulus–organism–response framework, the results suggest that a consumer's emotions can be a mediating factor in the purchase process. In this study, we identify and explore how store environment and emotional states may influence various dimensions of purchase behavior. This research confirms that although cognitive factors may largely account for store selection and for most planned purchases within the store, the environment in the store and the emotional state of consumers may be important determinants of purchase behavior. This research has many pragmatic applications, because pleasure was associated with the amount of money spent and affinity for the store, whereas arousal was associated with money spent in the store, time spent in the store, and the number of items purchased in the store. © 1997 John Wiley & Sons, Inc.

Store environment and consumer purchase behavior: Mediating role of consumer emotions

Points out that the aging population has captured the attention of marketers, but that many companies are still uncertain as to whether they should, or how they should, go about marketing to the mature market. Highlights some of the key issues marketers are confronted with and discusses reasons for implementation of certain strategies. Concludes that, for marketing strategies to be effective, they should take into consideration the heterogeneity of the mature market and the specific product or service being marketed. Uses the results of recent studies to illustrate how such target product‐specific strategies can be developed.

Targeting the mature market: opportunities and challenges

The purpose of this paper is to present a detailed and critical look at the evolution of online survey research since Evans and Mathur’s (2005) article on the value of online surveys. At that time, online survey research was in its early stages. Also covered are the present and future states of online research. Many conclusions and recommendations are presented.,The look back focuses on online surveys, strengths and weaknesses of online surveys, the literature on several aspects of online surveys and online survey best practices. The look ahead focuses on emerging survey technologies and methodologies, and new non-survey technologies and methodologies. Conclusions and recommendations are provided.,Online survey research is used more frequently and better accepted by researchers than in 2005. Yet, survey techniques are still regularly transformed by new technologies. Non-survey digital research is also more prominent than in 2005 and can better track actual behavior than surveys can. Hybrid surveys will be widespread in the future.,The paper aims to provide insights for researchers with different levels of online survey experience. And both academics and practitioners should gain insights.,Adhering to a strong ethics code is vital to gain respondents’ trust and to produce valid results.,Conclusions and recommendations are offered in these specific areas: defining concepts, understanding the future role of surveys, developing and implementing surveys and a survey code of ethics. The literature review cites more than 200 sources.

The value of online surveys: a look back and a look ahead

Three types of variable have been used to explain brand preference changes: consumer characteristics, marketing mix factors and situational influences. The study presented in this paper focuses on the relationship between life events experienced by individuals, resultant stress and lifestyle changes and changes in brand preferences. Based on theory and past research, a model is proposed and tested. The data support the notion that brand preference changes may be viewed as the outcome of adjustments to new life conditions and changes in consumption lifestyles that reflect consumer efforts to cope with stressful life changes. Implications for consumer research are also discussed. Copyright © 2003 Henry Stewart Publications.

Anil Mathur

Papers

The value of online surveys

Store environment and consumer purchase behavior: Mediating role of consumer emotions

Targeting the mature market: opportunities and challenges

The value of online surveys: a look back and a look ahead

Life events and brand preference changes