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TABLE I Classification problems

in Microtuning of Membership Functions: Accuracy vs Interpretability
by Javier G. Marín-Blázquez, Qiang Shen

Table 2 Classification of problems at UTD

in Practical utilities for monitoring multicast service availability
by Pavan Namburi A, Kamil Sarac A, Kevin Almeroth B
"... In PAGE 10: ... Finally, since mcroute is not currently available, we consulted the MBGP routing table at the RP at UTD to categorize problems as either MBGP or PIM-SM related problems. Table2 shows the final classification for the UTD receiver. The listed sources are from Table 1 which failed on (*, G) joins for the UTD receiver.... ..."

Table 1. Complexity of the classification problems.

in Supervised clustering and fuzzy decision tree induction for the identification of compact classifiers
by Ferenc Peter Pach, Janos Abonyi, Or Nemeth, Peter Arva 2004
Cited by 1

Table 4: Multiclass classification problems.

in Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problems
by Marc Sebban, Richard Nock, Stéphane Lallich, E. Brodley, Andrea Danyluk 2002
"... In PAGE 17: ... 7.2 Experimental Results Table4 presents the properties (name, number of classes, learning set size and number of features) of the eight tested datasets. In order to assess the relevance of our multiclass statistical test, we... ..."
Cited by 1

TABLE II CLASSIFICATION OF PROBLEMS AT UTD

in Practical utilities for monitoring multicast service availability
by Pavan Namburi, Kamil Sarac 2005
Cited by 1

Table 2. Properties of classification problems and Datasets.

in Comparison of descriptor spaces for chemical compound retrieval and classification
by Nikil Wale, Ian A. Watson, George Karypis 2006
"... In PAGE 9: ...The performance of the different descriptors and kernel functions was assessed on 28 different classification problems from 18 different datasets. The size, distri- bution and compound characteristics of the 28 classification problems are shown in Table2 . Each of the 28 classification problems is unique in that it has different distribution of positive class (ranging from 1% in H2 to 50% in C1), different number of compounds (ranging from the smallest with 559 compounds to largest with 78,995 compounds) and compounds of different average sizes (ranging from the 14 atoms per compound to 37 atoms per compound on an average in C1 and H3 respectively).... ..."

Table 1: Comparative results for the classification problems

in unknown title
by unknown authors 1997
"... In PAGE 3: ... For the LVQ method we have used the LVQ1 algo- rithm [12] with learning rate 0:03. Table1 reports testing results for the three data collec- tions and for the two proposed approaches in comparison with the BP and LVQ methods. The best number of clus- ters found and the classification rate for the testing sets are displayed.... ..."
Cited by 1

Table 1: Comparative results for the classification problems

in unknown title
by unknown authors 1997
"... In PAGE 3: ... For the LVQ method we have used the LVQ1 algo- rithm [12] with learning rate 0:03. Table1 reports testing results for the three data collec- tions and for the two proposed approaches in comparison with the BP and LVQ methods. The best number of clus- ters found and the classification rate for the testing sets are displayed.... ..."
Cited by 1

Table 1: Summary of the results on the binary classification problems.

in Evolving the Architecture and Weights of Neural Networks Using a Weight Mapping Approach
by Jo Carlos, João Carlos Figueira Pujol, Riccardo Poli
"... In PAGE 8: ... For each problem, 50 runs were performed with different random seeds. A summary of the results obtained is shown in Table1 . Column 2 represents the average number of generations (standard deviation in brackets).... ..."

TABLE I Performances in the toy classification problem I.

in Averaging, Maximum Penalized Likelihood and Bayesian Estimation for Improving Gaussian Mixture Probability Density Estimates
by Dirk Ormoneit, Volker Tresp 1996
Cited by 31
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