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

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

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Convex Analysisの二,三の進展について

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques To improve performance, these methods have sometimes been combined in hybrid recommenders This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering

Hybrid Recommender Systems: Survey and Experiments

An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. The notion of ontologies is crucial for the purpose of enabling knowledge sharing and reuse. The Handbook on Ontologies provides a comprehensive overview of the current status and future prospectives of the field of ontologies considering ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications that are among the best of their kind. The field of ontologies has tremendously developed and grown in the five years since the first edition of the "Handbook on Ontologies". Therefore, its revision includes 21 completely new chapters as well as a major re-working of 15 chapters transferred to this second edition.

Handbook on Ontologies

1. Introduction.- 2. Experience Management.- 3. Representing Experience.- 4. Assessing Experience Utility.- 5. Representing Knowledge for Adaptation.- 6. User Communication.- 7. Experience Retrieval.- 8. Experience Adaptation.- 9. Developing and Maintaining Experience Management Applications.- 10. Experience Management for Electronic Commerce.- 11. Experience Management for Self-Service and Help-Desk Support.- 12. Experience Management for Electronic Design Reuse.

Experience Management: Foundations, Development Methodology, and Internet-Based Applications

The resistivity {\it rho} $_{{\it xx}}$ measured in large antidot arrays as a function of an applied perpendicular magnetic field {\it B} displays distinct quantum oscillations at very low temperatures. These oscillations characteristically differ from conventional Shubnikov-de Haas oscillations which are periodic in 1/{\it B}. A crossover to {\it B } periodic oscillations at low {\it B} discloses the influence of the imposed potential. Applying semiclassical periodic orbit theory to the nonintegrable (chaotic) electron motion in the antidot lattice we attribute these phenomena to the quantization of few fundamental periodic orbits.

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Quantized periodic orbits in large antidot arrays.

Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology (Lecture Notes in Computer Science, 1612.)

Object-oriented case representations require approaches for similarity assessment that allow to compare two differently structured objects, in particular, objects belonging to different object classes. Currently, such similarity measures are developed more or less in an ad-hoc fashion. It is mostly unclear, how the structure of an object-oriented case model, e.g., the class hierarchy, influences similarity assessment. Intuitively, it is obvious that the class hierarchy contains knowledge about the similarity of the objects. However, how this knowledge relates to the knowledge that could be represented in similarity measures is not obvious at all. This paper analyzes several situations in which class hierarchies are used in different ways for case modeling and proposes a systematic way of specifying similarity measures for comparing arbitrary objects from the hierarchy. The proposed similarity measures have a clear semantics and are computationally inexpensive to compute at run-time.

https://kluedo.ub.uni-kl.de/frontdoor/deliver/index/docId/126/file/no_series_129.pdf

Similarity Measures for Object-Oriented Case Representations

From the Publisher:
In only a few years time, case-based reasoning has evolved from a research topic studied at a small number of specialized academic labs into an industrial-strength technology that holds great promise for the development of intelligent solutions to business problems. The INRECA methodology presented in detail in this monograph provides a data analysis framework for developing case-based reasoning solutions for successful application in real-world industrial contexts." "The book comes with a CD-ROM containing a wealth of supplementary sample material and successful application scenarios. Together, the book and the CD-ROM provide a self-contained introduction to case-based reasoning applications that addresses both R&D professionals and general IT managers interested in this promising new technology.

Ralph Bergmann

Papers

Experience Management: Foundations, Development Methodology, and Internet-Based Applications

Quantized periodic orbits in large antidot arrays.

Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology (Lecture Notes in Computer Science, 1612.)

Similarity Measures for Object-Oriented Case Representations

Developing Industrial Case-Based Reasoning Applications: The INRECA Methodology