Automatically assigned DDC number: 00631

Manually assigned DDC number: 00631

Number of references: 29

Title: E4 - Machine Learning

Author:

Subject: Pedro Domingos E4 - Machine Learning

Description: Machine learning's focus on ill-defined problems and highly flexible methods makes it ideally suited for KDD applications. Among the ideas machine learning contributes to KDD are the importance of empirical validation, the impossibility of learning without a priori assumptions, and the utility of limited-search or limited-representation methods. Machine learning provides methods for incorporating knowledge into the learning process, changing and combining representations, combatting the curse of dimensionality, and learning comprehensible models. KDD challenges for machine learning include scaling up its algorithms to large databases, using cost information in learning, automating data pre-processing, and enabling rapid development of applications. KDD opens up new directions for machine learning research, and brings new urgency to others. These directions include interfacing with the human user and the database system, learning from non-attribute-vector data, learning partial models, ...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1999-08-24

Format: ps

Identifier: http://citeseer.ist.psu.edu/205450.html

Source: http://www.gia.ist.utl.pt/~pedrod/hdmkd.ps.gz

Language: en

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