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

Data Mining Practical Machine Learning Tools and Techniques

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

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

Beyond the hype

An empirical study of the factors affecting social network service use

Data quality management, data usage experience and acquisition intention of big data analytics

Intimacy, familiarity and continuance intention: An extended expectation-confirmation model in web-based services

Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products

As the need to analyze big data sets grows dramatically, the role that classification algorithms play in data mining techniques also increases. Big data analysis requires more of the data sets' characteristics to be included, such as data structure, variety of sources, and the rate of update frequency. In this paper, we evaluate scenarios that examine which data set characteristics most affect the classification algorithms' performance. It is still a complex issue to determine which algorithm is how strong or how weak in relation to which data set. Thus, our research experimentally examines how data set characteristics affect algorithm performance, both in terms of accuracy and in elapsed time. To do so, we use a multiple regression method to evaluate the causality between data set characteristics as independent variables, and performance metrics as dependent variables. We also examine the role that classification algorithms play as moderator in this causality. All benchmark data sets in a UCI database are used that are fit to run the classification algorithm. Based on the results of the experiment, we discuss the requirements of legacy classification algorithms to address big data analysis in a new business intelligence era.

Ohbyung Kwon

Papers

An empirical study of the factors affecting social network service use

Data quality management, data usage experience and acquisition intention of big data analytics

Intimacy, familiarity and continuance intention: An extended expectation-confirmation model in web-based services

Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products

Effects of data set features on the performances of classification algorithms