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

The measurement of meaning

行銷硏究 : Marketing research

From Marketing Mix to Relationship Marketing: Towards a Paradigm Shift in Marketing

By Pierre Bourdieu (London: Routledge, 2010), xxx + 607 pp. £15.99 paper. A combination of social theory, statistical data, illustrations, and interviews, Distinction: A Social Critique of the Judg...

Distinction: A Social Critique of the Judgement of Taste

Brand personality: How to make the metaphor fit?

The role of SME entrepreneurs’ innovativeness and personality in the adoption of innovations

This study analyzes the impact of ethical motivations, food safety and health-related concerns on purchasing intentions of habitual and less frequent consumers of organic food. A sample of 291 subjects was surveyed through a paper-and-pencil questionnaire and classified either as “regular” or “occasional” purchasers of organic food according to their buying frequency. Results show different determinants of intention for the two groups of subjects: ethical motivations affect the purchase intentions of regular consumers, whereas food safety concerns influence the purchase intentions of occasional consumers. Implications are discussed. The market for organic food continues to expand worldwide at an average rate of 20% annually. Currently, over thirty-seven million hectares of land worldwide are managed organically by roughly two million farmers. In Europe, more than 250,000 farms manage around ten million hectares of organic land, with the highest share of organic agricultural land found in Spain, Italy and Germany (Willer and Kilcher 2011). In the last decades, this increasing popularity has fueled the growth of a multidisciplinary stream of research that has investigated the psychological and anthropological drivers of organic food consumption. Several studies have concluded that ethical principles, such as ecological sustainability and care for animal welfare—which constitute a sort of “inspirational framework” for organic farming—drive consumers’ choices of organic food. Other studies posit that organic food purchase behavior is motivated by the perceived healthiness of such products (Guido 2009; Guido et al.

Determinants of Regular and Occasional Consumers' Intentions to Buy Organic Food

Internet of Things and Big Data as enablers for business digitalization strategies

Purpose – The purpose of this paper is to investigate the latent determinants of the purchasing intention for fashion luxury goods.Design/methodology/approach – A total of 40 in‐depth interviews were conducted with Italian customers in a fashion luxury goods store in Italy. Qualitative research was employed in data collection using the laddering technique and the means‐end chain (MEC) analysis.Findings – Results showed that consumers buy luxury fashion goods mainly to match their lifestyle, thus satisfying their inner drives. The hierarchical value map resulting from the data collection and elaboration demonstrates that self‐confidence and self‐fulfillment are the main hidden final values when buying and consuming luxury goods. Implications for marketers are related to tailoring products, brand values and communication messages to the subjective, self‐rewarding and “internalized” consumption sought after by consumers.Originality/value – The contribution of the paper is based on the use of the laddering te...

Gianluigi Guido

Papers

Brand personality: How to make the metaphor fit?

The role of SME entrepreneurs’ innovativeness and personality in the adoption of innovations

Determinants of Regular and Occasional Consumers' Intentions to Buy Organic Food

Internet of Things and Big Data as enablers for business digitalization strategies

Determinants of purchasing intention for fashion luxury goods in the Italian market