There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

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“Bioinformatics” 특집을 내면서

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

A morphable model for the synthesis of 3D faces

Unsupervised segmentation of images which are composed of various textures is investigated. A coarse segmentation is achieved through a hierarchical self organizing map. This initial segmentation result is fed into a simulated annealing algorithm in which region and texture parameters are estimated using a maximum likelihood technique. Region geometries are modeled as Potts model while textures are modeled as Markov random fields. Tests are performed on artificial textured images.

Unsupervised texture based image segmentation by simulated annealing using Markov random field and Potts models

In this paper our aim is to develop a logical representation for form documents. We propose a hierarchical structure to represent the logical layout of a form by using lines. The approach is top-down and no domain knowledge such as the preprinted data or filled-in data is used. Logically same but physically different forms are associated to the same hierarchical tree. This representation can handle geometrical modifications and slight variations.

A heuristic algorithm for hierarchical representation of form documents

A matching algorithm based on linear features

http://homepage.tudelft.nl/a9p19/papers/prl_04_mcpa.pdf

Almost autonomous training of mixtures of principal component analyzers

This paper presents particularly a contextual postprocessing subsystem for a Turkish machine printedcharacter recognition system. The contextual postprocessing subsystem is based on positional binary 3-gram statistics for Turkish language, an error correctorparser and a lexicon, which contains root words and theinflected forms of the root words. Error corrector parseris used for correcting CR alternatives using TurkishMorphology.

Volkan Atalay

Papers

Unsupervised texture based image segmentation by simulated annealing using Markov random field and Potts models

A heuristic algorithm for hierarchical representation of form documents

A matching algorithm based on linear features

Almost autonomous training of mixtures of principal component analyzers

A character recognizer for Turkish language