Computational Intelligence in Space Applications

Aug 24, 2012

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Computational Intelligence in Space Applications

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1. Computational Intelligence in Space Applications Rita A. Ribeiro

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 19 Pruning of decision trees Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Pruning. How to avoid overfitting and deal with the noise in data?.

317 views • 12 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 32 Nearest neighbor methods Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. k-NN .

365 views • 18 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 37 Summary & review Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Overview. First I was talking about General Introduction to computational intelligence, problems and inspirations how to solve them.

290 views • 12 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 25 Kernel methods Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Kernels!.

307 views • 15 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 29 Approximation theory, RBF and SFN networks Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Basis set functions. A combination of m functions. may be used for discrimination or for density estimation.

300 views • 17 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 6 Principal Component Analysis. Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Linear transformations – example. 2D vectors X uniformly distributed in a unit circle with mean (1,1);

352 views • 20 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 17 WEKA/RapidMiner Knowledge extraction from simplest decision trees Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. A few data mining packages.

446 views • 23 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 4 CI: simple visualization. Source: Włodzisław Duch ; Dept. of Informatics, UMK ; Google: W Duch. 2D projections: scatterplots. Simplest projections: use scatterplots, select only 2 features.

637 views • 50 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 28 Non-parametric density modeling Source: Włodzisław Duch ; Dept. of Informatics, UMK ; Google: W Duch. Density from histograms.

472 views • 35 slides

Computational Intelligence: Methods and Applications

Lecture 7 Discriminant Component Analysis Source: Włodzisław Duch ; Dept. of Informatics, UMK ; Google: W Duch. Computational Intelligence: Methods and Applications. PCA problem. PCA transformation for 2D data:.

734 views • 52 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 25 Kernel methods Source: Włodzisław Duch ; Dept. of Informatics, UMK ; Google: W Duch. Kernels!.

487 views • 34 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 37 Summary & review Source: Włodzisław Duch ; Dept. of Informatics, UMK ; Google: W Duch. Overview.

720 views • 54 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 19 Pruning of decision trees Source: Włodzisław Duch ; Dept. of Informatics, UMK ; Google: W Duch. Pruning. How to avoid overfitting and deal with the noise in data?.

608 views • 41 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 13 Bayesian risks & N aive Bayes approach Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Bayesian future.

280 views • 18 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 1 Organization and overview Włodzisław Duch Dept. of Informatics, UMK Google: W Duch.

243 views • 9 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 1 0 SOM and Growing Cell Structures Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Italian olive oil. An example of SOM application:. 572 samples of olive oil were collected from 9 Italian provinces .

231 views • 15 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 24 SVM in the non-linear case Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Non-separable picture.

217 views • 10 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 9 Self-Organized Mappings Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Brain maps. Tactile, motor, and olfactory data are most basic.

337 views • 19 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 1 6 Model evaluation and ROC Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Confusion matrices.

404 views • 18 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 35 Filters for feature selection and discretization methods. Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Filters & Wrappers. Filter approach: define your problem, for example assignment of class labels;

252 views • 16 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 4 CI: simple visualization. Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. 2D projections: scatterplots. Simplest projections: use scatterplots, select only 2 features. Example: sugar – teeth decay.

238 views • 16 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 34 Applications of information theory and selection of information Włodzisław Duch Dept. of Informatics, UMK Google: W Duch. Some applications of info theory.

138 views • 12 slides

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