Andere Kunden interessierten sich auch für
![Relation Extraction from Web Texts with Linguistic and Web Features]( "Relation Extraction from Web Texts with Linguistic and Web Features")
Relation Extraction from Web Texts with Linguistic and Web Features46,99 €![LINEAR DATA MINING MODEL WITH VERY FEW MINIMUM GENE FEATURES]( "LINEAR DATA MINING MODEL WITH VERY FEW MINIMUM GENE FEATURES")
LINEAR DATA MINING MODEL WITH VERY FEW MINIMUM GENE FEATURES32,99 €![Analyzing the Features of Java And Map Reduce on Hadoop]( "Analyzing the Features of Java And Map Reduce on Hadoop")
Analyzing the Features of Java And Map Reduce on Hadoop26,99 €![Hand Vein Recognition by Combination of Local and Statistical Features]( "Hand Vein Recognition by Combination of Local and Statistical Features")
Hand Vein Recognition by Combination of Local and Statistical Features36,99 €![Emotion Recognition using Prosodic and Spectral Features of Speech]( "Emotion Recognition using Prosodic and Spectral Features of Speech")
Emotion Recognition using Prosodic and Spectral Features of Speech32,99 €![FEATURES OF TEACHING COMPUTER MODELING IN INFORMATICS LESSONS]( "FEATURES OF TEACHING COMPUTER MODELING IN INFORMATICS LESSONS")
FEATURES OF TEACHING COMPUTER MODELING IN INFORMATICS LESSONS39,99 €![Ameliorated Feature-Oriented RBAC Permission Evolution Handling]( "Ameliorated Feature-Oriented RBAC Permission Evolution Handling")
Ameliorated Feature-Oriented RBAC Permission Evolution Handling32,99 €
The automatic analysis of data acquired with hyperdimensional sensors is rather challenging since it should be carried out in hyperdimensional feature spaces. The huge size of the feature space involves the so-called curse of dimensionality. This latter is due to the unbalancing between the number of features and the number of samples. In this book, it is proposed to exploit the whole information available in the original hyperdimensional feature space by means of the fusion of multiple regression methods. The development of the proposed multiple regression systems will include three main steps. The first one is related to the partition of the original hyperdimensional feature space into subspaces of reduced dimensionality. The second step consists in training in each of the subspaces obtained in the previous step a regression method. Finally, in the third and final step, the results provided by the different regression methods will be combined in order to produce a global estimate of the physical parameter of interest with an expected higher accuracy with respect to what can be achieved by the classical regression approach based on feature selection.