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Using Multivariate Statistics

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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The five-factor model of personality is a hierarchical organization of personality traits in terms of five basic dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience. Research using both natural language adjectives and theoretically based personality questionnaires supports the comprehensiveness of the model and its applicability across observers and cultures. This article summarizes the history of the model and its supporting evidence; discusses conceptions of the nature of the factors; and outlines an agenda for theorizing about the origins and operation of the factors. We argue that the model should prove useful both for individual assessment and for the elucidation of a number of topics of interest to personality psychologists.

/pdf/an-introduction-to-the-five-factor-model-and-its-1hd8x7eums.pdf

An introduction to the five-factor model and its applications.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Personality, well-being and health correlates of trait emotional intelligence

The dark triad and normal personality traits

Intelligence and the differentiation hypothesis

Moral disengagement, the dark triad, and unethical consumer attitudes

The NEO Five Factor Inventory (NEO-FFI) was given to 1025 British subjects as part of three independent research studies. Data from these studies were pooled and subjected to item-level analyses. Using standard scoring criteria from the measure provisional British norms were produced which were broadly equivalent to those obtained in the USA. The individual subscales showed good internal consistency. However, the item-level principal components analysis using varimax and oblique rotation and confirmatory factor analysis revealed that only the Neuroticism, Agreeableness and Conscientiousness traits were coherently represented in the main factors derived by the analysis. Openness and Extraversion factors did not show such stability or consistency. It is argued that as a result of these difficulties, thoughtlessly embracing the NEO-FFI as a quick and efficient instrument for measuring the ‘Big Five’ personality traits is perhaps premature, as the instrument requires modification and improvement before it can truly be regarded as measuring five independent personality traits.

Vincent Egan

Papers

Personality, well-being and health correlates of trait emotional intelligence

The dark triad and normal personality traits

Intelligence and the differentiation hypothesis

Moral disengagement, the dark triad, and unethical consumer attitudes

The NEO-FFI: emerging British norms and an item-level analysis suggest N, A and C are more reliable than O and E