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  • 1
    Electronic Resource
    Electronic Resource
    Complexity–Based Induction (1994)
    Springer
    Machine learning 16 (1994), S. 203-225 
    Details
    ISSN: 0885-6125
    Keywords: Inductive logic programming ; data compression ; minimum description length principle ; model complexity ; learning from positive–only examples ; theory preference criterion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A central problem in inductive logic programming is theory evaluation. Without some sort of preference criterion, any two theories that explain a set of examples are equally acceptable. This paper presents a scheme for evaluating alternative inductive theories based on an objective preference criterion. It strives to extract maximal redundancy from examples, transforming structure into randomness. A major strength of the method is its application to learning problems where negative examples of concepts are scarce or unavailable. A new measure called model complexity is introduced, and its use is illustrated and compared with a proof complexity measure on relational learning tasks. The complementarity of model and proof complexity parallels that of model and proof–theoretic semantics. Model complexity, where applicable, seems to be an appropriate measure for evaluating inductive logic theories.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022641209111
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Complexity-based induction (1994)
    Springer
    Machine learning 16 (1994), S. 203-225 
    Details
    ISSN: 0885-6125
    Keywords: Inductive logic programming ; data compression ; minimum description length principle ; model complexity ; learning from positive-only examples ; theory preference criterion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A central problem in inductive logic programming is theory evaluation. Without some sort of preference criterion, any two theories that explain a set of examples are equally acceptable. This paper presents a scheme for evaluating alternative inductive theories based on an objective preference criterion. It strives to extract maximal redundancy from examples, transforming structure into randomness. A major strength of the method is its application to learning problems where negative examples of concepts are scarce or unavailable. A new measure calledmodel complexity is introduced, and its use is illustrated and compared with aproof complexity measure on relational learning tasks. The complementarity of model and proof complexity parallels that of model and proof-theoretic semantics. Model complexity, where applicable, seems to be an appropriate measure for evaluating inductive logic theories.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993307
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Technical Note: Naive Bayes for Regression (2000)
    Springer
    Machine learning 41 (2000), S. 5-25 
    Details
    ISSN: 0885-6125
    Keywords: naive Bayes ; regression ; model trees ; linear regression ; locally weighted regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Despite its simplicity, the naive Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates. This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces “model trees”—decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naive Bayes is the method of choice, on real-world datasets it is almost uniformly worse than locally weighted linear regression and model trees. The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes applied to regression problems by discretizing the target value performs similarly badly. We then present empirical evidence that isolates naive Bayes' independence assumption as the culprit for its poor performance in the regression setting. These results indicate that the simplistic statistical assumption that naive Bayes makes is indeed more restrictive for regression than for classification.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007670802811
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    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Using Model Trees for Classification (1998)
    Springer
    Machine learning 32 (1998), S. 63-76 
    Details
    ISSN: 0885-6125
    Keywords: Model trees ; classification algorithms ; M5 ; C5.0 ; decision trees
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007421302149
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    Paper (German National Licenses)
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  • 5
    Electronic Resource
    Electronic Resource
    Greenstone: open-source digital library software with end-user collection building (2001)
    Bradford : Emerald
    Online information review 25 (2001), S. 288-298 
    Details
    ISSN: 1468-4527
    Source: Emerald Fulltext Archive Database 1994-2005
    Topics: Information Science and Librarianship
    Notes: The Greenstone digital library software is an open-source system for the construction and presentation of information collections. Collections built with Greenstone offer effective full-text searching and metadata-based browsing facilities that are attractive and easy to use. Moreover, they are easily maintainable and can be augmented and rebuilt entirely automatically. The system is extensible: software "plugins" accommodate different document and metadata types. Greenstone incorporates an interface that makes it easy for people to create their own library collections. Collections may be built and served locally from the user's own Web server, or (given appropriate permissions) remotely on a shared digital library host. End users can easily build new collections styled after existing ones from material on the Web or from their local files (or both), and collections can be updated and new ones brought online at any time.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1108/14684520110410490
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    Paper (German National Licenses)
    Fulltext
  • 6
    Electronic Resource
    Electronic Resource
    Creating digital library collections with Greenstone (2005)
    Bradford : Emerald
    Library hi tech 23 (2005), S. 541-560 
    Details
    ISSN: 0737-8831
    Source: Emerald Fulltext Archive Database 1994-2005
    Topics: Information Science and Librarianship
    Notes: Purpose - The purpose of this paper is to introduce Greenstone and explain how librarians use it to create and customize digital library collections. Design/methodology/approach - Through an end-user interface, users may add documents and metadata to collections, create new collections whose structure mirrors existing ones, and build collections and put them in place for users to view. Findings - First-time users can easily and quickly create their own digital library collections. More advanced users can design and customize new collection structures Originality/value - The Greenstone digital library software is a comprehensive system for building and distributing digital library collections. It provides a way of organizing information based on metadata and publishing it on the Internet or on removable media such as CD-ROM/DVD.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1108/07378830510636337
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    Paper (German National Licenses)
    Fulltext
  • 7
    Electronic Resource
    Electronic Resource
    The promise of digital libraries in developing countries (2002)
    Bradford : Emerald
    The @electronic library 20 (2002), S. 7-13 
    Details
    ISSN: 0264-0473
    Source: Emerald Fulltext Archive Database 1994-2005
    Topics: Information Science and Librarianship
    Notes: Digital libraries are a key technology for developing countries. They can assist human development by providing a non-commercial mechanism for distributing humanitarian information on topics such as health, agriculture, nutrition, hygiene, sanitation and water supply. Many other areas, ranging from disaster relief to medical education, also benefit from new methods of information distribution. Perhaps even more important than disseminating information originating in the developed world is the need to foster the ability for people in developing countries to build information collections locally. Outlines a broad range of issues and then goes on to describe how a freely available digital library system called "Greenstone" provides a flexible tool that helps meet some of these needs. Being "open source" software, Greenstone can be shaped by its users to meet new requirements.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1108/02640470210418218
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    Paper (German National Licenses)
    Fulltext
  • 8
    Electronic Resource
    Electronic Resource
    Tune Retrieval in the Multimedia Library (2000)
    Springer
    Multimedia tools and applications 10 (2000), S. 113-132 
    Details
    ISSN: 1573-7721
    Keywords: music retrieval ; melody recall ; acoustic interfaces ; relevance ranking
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Musical scores are traditionally retrieved by title, composer or subject classification. Just as multimedia computer systems increase the range of opportunities available for presenting musical information, so they also offer new ways of posing musically-oriented queries. This paper shows how scores can be retrieved from a database on the basis of a few notes sung or hummed into a microphone. The design of such a facility raises several interesting issues pertaining to music retrieval. We first describe an interface that transcribes acoustic input into standard music notation. We then analyze string matching requirements for ranked retrieval of music and present the results of an experiment which tests how accurately people sing well known melodies. The performance of several string matching criteria are analyzed using two folk song databases. Finally, we describe a prototype system which has been developed for retrieval of tunes from acoustic input and evaluate its performance.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009606600500
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    Paper (German National Licenses)
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  • 9
    Electronic Resource
    Electronic Resource
    Lexically-generated subject hierarchies for browsing large collections (1999)
    Springer
    International journal on digital libraries 2 (1999), S. 111-123 
    Details
    ISSN: 1432-1300
    Keywords: Key words: Browsing – Hierarchies – Inference – Scalability – Intuition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Information Science and Librarianship
    Notes: Abstract. Developing intuition for the content of a digital collection is difficult. Hierarchies of subject terms allow users to explore the space of topics that a collection covers, to form and specialize useful query terms, and to directly identify interesting documents. We describe two interfaces for navigating such hierarchies, and present a technique for inferring hierarchies automatically from large corpora. We also discuss scalability issues for the techniques involved, and our solutions to these problems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s007990050041
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 10
    Electronic Resource
    Electronic Resource
    Programming by example: The human face of AI (1992)
    Springer
    AI & society 6 (1992), S. 166-180 
    Details
    ISSN: 1435-5655
    Keywords: Attention focusing ; Instructible systems ; Intentional stance ; Machine learning ; Programming by example ; Teaching metaphor
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract It is argued that “human-centredness” will be an important characteristic of systems that learn tasks from human users, as the difficulties in inductive inference rule out learning without human assistance. The aim of “programming by example” is to create systems that learn how to perform tasks from their human users by being shown examples of what is to be done. Just as the user creates a learning environment for the system, so the system provides a teaching opportunity for the user, and emphasis is placed as much on facilitating successful teaching as on incorporating techniques of machine learning. If systems can “learn” repetitive tasks, their users will have the power to decide for themselves which parts of their jobs should be automated, and teach the system how to do them — reducing their dependence on intermediaries such as system designers and programmers. This paper presents principles for programming by example derived from experience in creating four prototype learners: for technical drawing, text editing, office tasks, and robot assembly. A teaching metaphor (a) enables the user to demonstrate a task by performing it manually, (b) helps to explain the learner's limited capabilities in terms of a persona, and (c) allows users to attribute intentionality. Tasks are represented procedurally, and augmented with constraints. Suitable mechanisms for attention focusing are necessary in order to control inductive search. Hidden features of a task should be made explicit so that the learner need not embark on the huge search entailed by hypothesizing missing steps.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02472779
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    Paper (German National Licenses)
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