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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’

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Convolutional Neural Networks for Diabetic Retinopathy

Graph mining is an important research area within the domain of data mining The field of study concentrates on the identification of frequent subgraphs within graph data sets The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective This paper presents a survey of current research in the field of frequent subgraph mining and proposes solutions to address the main research issues

/pdf/a-survey-of-frequent-subgraph-mining-algorithms-46402iwjhg.pdf

A survey of frequent subgraph mining algorithms

A well-known approach to Knowledge Discovery in Databases involves the identification of association rules linking database attributes. Extracting all possible association rules from a database, however, is a computationally intractable problem, because of the combinatorial explosion in the number of sets of attributes for which incidence-counts must be computed. Existing methods for dealing with this may involve multiple passes of the database, and tend still to cope badly with densely-packed database records. We describe here a class of methods we have introduced that begin by using a single database pass to perform a partial computation of the totals required, storing these in the form of a set enumeration tree, which is created in time linear to the size of the database. Algorithms for using this structure to complete the count summations are discussed, and a method is described, derived from the well-known Apriori algorithm. Results are presented demonstrating the performance advantage to be gained from the use of this approach. Finally, we discuss possible further applications of the method.

Tree Structures for Mining Association Rules

Two new structures for association rule mining (ARM), the T-tree, and the P-tree, together with associated algorithms, are described. The authors demonstrate that the structures and algorithms offer significant advantages in terms of storage and execution time.

/pdf/data-structure-for-association-rule-mining-t-trees-and-p-5b7sasibae.pdf

Data structure for association rule mining: T-trees and P-trees

This paper discusses some engineering considerations that should be taken into account when building a knowledge based system, and recommends isomorphism, the well defined correspondence of the knowledge base to the source texts, as a basic principle of system construction in the legal domain Isomorphism, as it has been used in the field of legal knowledge based systems, is characterised and the benefits which stem from its use are described Some objections to and limitations of the approach are discussed The paper concludes with a case study giving a detailed example of the use of the isomorphic approach in a particular application

/pdf/isomorphism-and-legal-knowledge-based-systems-4e6lreli23.pdf

Frans Coenen

Papers

Convolutional Neural Networks for Diabetic Retinopathy

A survey of frequent subgraph mining algorithms

Tree Structures for Mining Association Rules

Data structure for association rule mining: T-trees and P-trees

Isomorphism and legal knowledge based systems