Name
Abstract | ||
|---|---|---|
Support Feature Machines (SFM) define useful features derived from similarity to support vectors (kernel transformations), global projections (linear or perceptron-style) and localized projections. Explicit construction of extended feature spaces enables control over selection of features, complexity control and allows final analysis by any classification method. Additionally projections of high-dimensional data may be used to estimate and display confidence of predictions. This approach has been applied to the DNA microarray data. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13529-3_20 | RSCTC |
Keywords | Field | DocType |
explicit construction,display confidence,high-dimensional data,support feature machines,global projection,classification method,complexity control,final analysis,support feature machine,extended feature space,dna microarray data,turing machine,high dimensional data,support vector machine,support vector,feature space | Structured support vector machine,Graph kernel,Data mining,Feature vector,Dimensionality reduction,Pattern recognition,Feature (computer vision),Support vector machine,Artificial intelligence,Relevance vector machine,Kernel method,Mathematics | Conference |
Volume | ISSN | ISBN |
6086 | 0302-9743 | 3-642-13528-5 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| tomasz maszczyk | 1 | 42 | 5.29 |
| Włodzisław Duch | 2 | 291 | 28.95 |