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Table 4 Adaptation of the Q-learning algorithm to AIES domain

in An Experience Applying Reinforcement Learning in a Web-Based Adaptive and Intelligent Educational System
by Ana Iglesias, Paloma Martínez, O Fernández 2003
"... In PAGE 10: ....2. Adaptation of the Q-learning Algorithm In this section, how the Q-learning algorithm is adapted to the tutor domain is explained. In Table4 a comparison of the theoretical Q-learning algorithm and the adapted Q-lear- ning algorithm to de AIES domain is presented. 5.... ..."
Cited by 1

Table 1a - Single machine learning features Feature Description

in Syntactic features for protein-protein interaction extraction
by Rune Sætre, Kenji Sagae
"... In PAGE 10: ...pdf Figure 3 - Features.pdf Tables Table1 - Language features used for the machine learning Table 1a - Single machine learning features Feature Description... In PAGE 10: ... Table1 b - Feature sets used for machine learning Feature Description set 1 feature type F1 Word-features: Before, Between, After F2 Path: Kenji Dependency between Enju Lemmas F3 Path: Enju PAS between Enju Lemmas 2 feature types F4 Dependency Parsing + Word-features F5 Enju + Word-features F6 2 Paths: Enju amp; Dependency Parsing All feature types F7... ..."

Table 1b - Feature sets used for machine learning Feature Description

in Syntactic features for protein-protein interaction extraction
by Rune Sætre, Kenji Sagae
"... In PAGE 10: ...pdf Figure 3 - Features.pdf Tables Table1 - Language features used for the machine learning Table 1a - Single machine learning features Feature Description After The set of tokens after the last mention of the last protein in the pair Before The set of tokens before the first mention of the first protein in the pair Between The set of tokens between the end of the first protein and the beginning of the last protein in the pair Head The final semantic head token (head of head of.... In PAGE 10: ...pdf Figure 3 - Features.pdf Tables Table 1 - Language features used for the machine learning Table1 a - Single machine learning features Feature Description After The set of tokens after the last mention of the last protein in the pair Before The set of tokens before the first mention of the first protein in the pair Between The set of tokens between the end of the first protein and the beginning of the last protein in the pair Head The final semantic head token (head of head of.... ..."

Table 4: Six features of useful machine learning software

in The Need for Open Source Software in Machine Learning
by Sören Sonnenburg, Mikio L. Braun, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann Lecun, Fernando Pereira, Carl Edward Rasmussen, Bernhard Schölkopf, Jason Weston, et al.
"... In PAGE 20: ...Table 4: Six features of useful machine learning software Good machine learning software should first of all be a good piece of software ( Table4 ). There exist many books on software design.... ..."

Table 1: Final set of optimized features chosen for machine learning

in unknown title
by unknown authors 2006
"... In PAGE 6: ... A detailed version of this tree is at the website [21]. Page 6 of 9 (page number not for citation purposes) The final set of optimized features is given in Table1 . The optimization runs for feature selection are discussed in mining a polymorphic position.... In PAGE 8: ... The software code will be portable to most platforms where Perl can be executed. The software has modules for: Extracting the ML features ( Table1 ) from the output files generated by the sequence assembly program (phred- (PolyBayes or PolyPhred) and creating a data file in the format required for C4.5 execution.... ..."

Table 2: Performance of the machine learning algo- rithms depending on feature set.

in Early Error Detection on Word Level
by Gabriel Skantze, Jens Edlund

Table 4: Six features of useful machine learning software

in The Need for Open Source Software in Machine Learning
by Sören Sonnenburg, Mikio L. Braun, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann Lecun, Fernando Pereira, Carl Edward Rasmussen, Bernhard Schölkopf, Jason Weston

Table 9: Results of machine learning using Rainbow.

in Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews
by Kushal Dave, Steve Lawrence, David M. Pennock 2003
"... In PAGE 5: ...fter selecting a set of features f1....fn and optionally smooth- ing their probabilities, we must assign them scores, used to place test documents in the set of positive reviews C or negative reviews Cprime. We tried some machine-learning techniques using the Rainbow text-classification package [10], but Table9 shows the performance was no better than our method. We also tried SVMlight, the package2 used by Pang et.... ..."
Cited by 96

Table II. Comparison of AEHS that use learning style as a source of adaptation System Domain Learning Style Model Adaptation based on Learning Style Diagnosis Approach amp; Dynamic Adaptation

in Accommodating learning style characteristics in Adaptive Educational Hypermedia
by K. A. Papanikolaou, M. Grigoriadou 2004
Cited by 2

Table 9: Results of machine learning using Rainbow.

in Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews
by Kushal Dave, Steve Lawrence, David M. Pennock 2003
"... In PAGE 5: ...6 Scoring After selecting a set of features a75 a37a18a76a16a76a16a76a16a76 a75 a28 and optionally smooth- ing their probabilities, we must assign them scores, used to place test documents in the set of positive reviews a3 or negative reviews a3 a10 . We tried some machine-learning techniques using the Rainbow text-classification package [11], but Table9 shows the performance was no better than our method. We also tried SVMa12a14a13a16a15a18a17a20a19 , the package2 used by Pang et al.... ..."
Cited by 96
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