2010, ISBN: 9783639288681

[ED: Taschenbuch / Paperback], [PU: VDM Verlag Dr. Müller], High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because th… Meer...

[ED: Taschenbuch / Paperback], [PU: VDM Verlag Dr. Müller], High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies., DE, [SC: 0.00], Neuware, gewerbliches Angebot, H: 220mm, B: 150mm, T: 6mm, 124, [GW: 185g], Selbstabholung und Barzahlung, PayPal, Offene Rechnung, Banküberweisung, Internationaler Versand<

ISBN: 9783639288681

[ED: Taschenbuch], [PU: VDM Verlag Dr. Müller], Neuware - High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the … Meer...

[ED: Taschenbuch], [PU: VDM Verlag Dr. Müller], Neuware - High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 220x150x6 mm, 124, [GW: 185g], PayPal, Offene Rechnung, Banküberweisung, Internationaler Versand<

ISBN: 9783639288681

[ED: Taschenbuch], [PU: VDM Verlag Dr. Müller], Neuware - High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the … Meer...

[ED: Taschenbuch], [PU: VDM Verlag Dr. Müller], Neuware - High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 220x150x6 mm, 124, [GW: 181g], Banküberweisung, PayPal, Internationaler Versand<

2010, ISBN: 9783639288681

[ED: Softcover], [PU: VDM Verlag Dr. Müller], High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous num… Meer...

[ED: Softcover], [PU: VDM Verlag Dr. Müller], High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies. 2010. 124 S. 220 mm Versandfertig in 6-10 Tagen, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Offene Rechnung (Vorkasse vorbehalten)<

ISBN: 9783639288681

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventio… Meer...

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies. New Textbooks>Trade Paperback>Science>Statistics & Probability>Statistics & Probability, VDM Verlag Core >1 >T<