The parameters used for each case are (a) C = 2, Ne = 100 and α = 3.3. (b) C = 2, Ne = 100 and α = 2.3. (c) C = 10, Ne = 50 and α = 4.3. (d) C = 10, Ne = 50 and α = 6.3. Note that each class can present highly distinct properties due to differences in correlation between their features.</p
We present 20 new multi-labeled artificial datasets, which can also be used for evaluating ambiguity...
A central problem in machine learning is identifying a representative set of features from which to ...
In many datasets, there is a very large number of attributes (e.g. many thousands). Such datasets ca...
<p>It is possible to note that different classes have different correlations between the features. T...
The following thesis explores the impact of the dataset distributional prop- erties on classificatio...
<p>We use one of the artificial datasets and the kNN classifier. (a) By randomly drawing 1,000 diffe...
<p>The table represents the results of the three clustering measures (QLC, TE and HS) over 10 NJ rec...
<p>The inter-individual variability is controlled by two parameters: i) the locations of the middle ...
This file contains a number of randomly generated datasets. The properties of each dataset are indi...
<p>To demonstrate how different parts of a class overlap with other classes in different feature spa...
Rosch (1975) proposed that some exemplars are more typical of a category than others. Typicality gra...
The evaluation of combination methods for multi-classifier systems is a difficult problem. In many c...
Example of the application of the Complex Correlation Measure (CCM) algorithm in a subset of 5 data ...
A method of automatic classification is developed for the case in which the features used to determi...
<p>(a) to (d): The results of kCCA. Vertical axes are the coefficients used for linear combinations ...
We present 20 new multi-labeled artificial datasets, which can also be used for evaluating ambiguity...
A central problem in machine learning is identifying a representative set of features from which to ...
In many datasets, there is a very large number of attributes (e.g. many thousands). Such datasets ca...
<p>It is possible to note that different classes have different correlations between the features. T...
The following thesis explores the impact of the dataset distributional prop- erties on classificatio...
<p>We use one of the artificial datasets and the kNN classifier. (a) By randomly drawing 1,000 diffe...
<p>The table represents the results of the three clustering measures (QLC, TE and HS) over 10 NJ rec...
<p>The inter-individual variability is controlled by two parameters: i) the locations of the middle ...
This file contains a number of randomly generated datasets. The properties of each dataset are indi...
<p>To demonstrate how different parts of a class overlap with other classes in different feature spa...
Rosch (1975) proposed that some exemplars are more typical of a category than others. Typicality gra...
The evaluation of combination methods for multi-classifier systems is a difficult problem. In many c...
Example of the application of the Complex Correlation Measure (CCM) algorithm in a subset of 5 data ...
A method of automatic classification is developed for the case in which the features used to determi...
<p>(a) to (d): The results of kCCA. Vertical axes are the coefficients used for linear combinations ...
We present 20 new multi-labeled artificial datasets, which can also be used for evaluating ambiguity...
A central problem in machine learning is identifying a representative set of features from which to ...
In many datasets, there is a very large number of attributes (e.g. many thousands). Such datasets ca...