COPYRIGHT YEAR

2012

AUTHORS

Luc Raedt

TITLE

Declarative Modeling for Machine Learning and Data Mining

ABSTRACT

Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem. Throughout the talk, I shall use illustrations from our work on constraint programming for itemset mining and probabilistic programming.

Related objects

How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

Download the RDF metadata as:   json-ld nt turtle xml License info


25 TRIPLES      25 PREDICATES      21 URIs      12 LITERALS

Subject Predicate Object
1 book-chapters:7c42b53f620b6c5038f6a5abf3329751 sg:abstract Abstract Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem. Throughout the talk, I shall use illustrations from our work on constraint programming for itemset mining and probabilistic programming.
2 sg:abstractRights OpenAccess
3 sg:bibliographyRights Restricted
4 sg:bodyHtmlRights Restricted
5 sg:bodyPdfRights Restricted
6 sg:copyrightHolder Springer-Verlag Berlin Heidelberg
7 sg:copyrightYear 2012
8 sg:ddsId Chap2
9 sg:doi 10.1007/978-3-642-34106-9_2
10 sg:esmRights OpenAccess
11 sg:hasBook books:205f06b1694c5db0da6ae9330e5ff8de
12 sg:hasBookEdition book-editions:feb82beab87e058ee62200c797f72d40
13 sg:hasContributingOrganization grid-institutes:grid.5596.f
14 sg:hasContribution contributions:45926039c5fff8b3ac2a81774e84ea8e
15 sg:language En
16 sg:license http://scigraph.springernature.com/explorer/license/
17 sg:metadataRights OpenAccess
18 sg:pageFirst 12
19 sg:pageLast 12
20 sg:scigraphId 7c42b53f620b6c5038f6a5abf3329751
21 sg:title Declarative Modeling for Machine Learning and Data Mining
22 sg:webpage https://link.springer.com/10.1007/978-3-642-34106-9_2
23 rdf:type sg:BookChapter
24 rdfs:label BookChapter: Declarative Modeling for Machine Learning and Data Mining
25 owl:sameAs http://lod.springer.com/data/bookchapter/978-3-642-34106-9_2
HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular JSON format for linked data.

curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/book-chapters/7c42b53f620b6c5038f6a5abf3329751'

N-Triples is a line-based linked data format ideal for batch operations .

curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/book-chapters/7c42b53f620b6c5038f6a5abf3329751'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/book-chapters/7c42b53f620b6c5038f6a5abf3329751'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/book-chapters/7c42b53f620b6c5038f6a5abf3329751'






Preview window. Press ESC to close (or click here)


...