Figure 8: Feature Space Representation [11][9].
Note the legend is not described as they are sample plotting to make understand the concepts involved.
As an example of this last point, consider the feature space shown to the right. The variables may each be regarded at two different resolutions.
As the foregoing illustration shows, every region of the feature space is covered only by a small number of gaussians.
The nearest neighbor algorithm is basically a refinement of clustering in the sense that they both use distance in some feature space to create either structure in the data or predictions.
Some algorithms, like k-means, simply partition the feature space. Other algorithms, like single-link agglomeration, create nested partitionings which form a taxonomy.
Because of the nature of the feature space in which these boundaries are found, Support Vector Machines can exhibit a large degree of flexibility in handling classification and regression tasks of varied complexities.
Related Posts:
Generalization and Symbolic Processing in Neural Networks
Word Learning in Feature Space
Verbal Labeling and Proactive Interference
Labels As an Accelerator of Ontological Development ...
In mathematics, a classifier is a mapping from a (discrete or continuous) feature space X to a discrete set of labels Y. Classifiers have practical applications in many branches of science and society. ...
 See also: Classification, Distribution, Machine learning, Regression, Neural network
  
|
RSS
