Example doc updates

shogun-toolbox · Aug 28, 2011 · f949f36 · f949f36
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diff --git a/examples/descriptions/modular/classifier_averaged_perceptron.txt b/examples/descriptions/modular/classifier_averaged_perceptron.txt
diff --git a/examples/descriptions/modular/preprocessor_hessianlocallylinearembedding.txt b/examples/descriptions/modular/preprocessor_hessianlocallylinearembedding.txt
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+In this example toy data is being preprocessed using the Hessian Locally Linear Embedding algorithm
+as described in
+
+Donoho, D., & Grimes, C. (2003). 
+Hessian eigenmaps: new tools for nonlinear dimensionality reduction.
+Proceedings of National Academy of Science (Vol. 100, pp. 5591-5596).
diff --git a/examples/descriptions/modular/preprocessor_isomap.txt b/examples/descriptions/modular/preprocessor_isomap.txt
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+In this example toy data is being processed using the Isomap algorithm
+as described in
+
+Silva, V. D., & Tenenbaum, J. B. (2003). 
+Global versus local methods in nonlinear dimensionality reduction. 
+Advances in Neural Information Processing Systems 15, 15(Figure 2), 721-728. MIT Press. 
+Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.3407&rep=rep1&type=pdf
+
+Before applying to the data the landmark approximation is enabled with 
+specified number of landmarks. The landmark approximation is described in
+
+Sparse multidimensional scaling using landmark points
+V De Silva, J B Tenenbaum (2004) Technology, p. 1-4
+
+After enabling the landmark approximation k parameter -- the number 
+of neighbors in the k nearest neighbor graph -- is initialized.
diff --git a/examples/descriptions/modular/preprocessor_kernellocallylinearembedding.txt b/examples/descriptions/modular/preprocessor_kernellocallylinearembedding.txt
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+In this example toy data is being processed using kernel extension
+of the Locally Linear Embedding (LLE) algorithm as described in
+
+Kayo, O. (2006). Locally linear embedding algorithm. Extensions and applications. October.
+Retrieved from: http://herkules.oulu.fi/isbn9514280415/isbn9514280415.pd
+
+Linear kernel is used as kernel of the extension.
diff --git a/examples/descriptions/modular/preprocessor_kernelpca.txt b/examples/descriptions/modular/preprocessor_kernelpca.txt
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+In this example toy data is being processed using the kernel PCA algorithm
+as described in
+
+Schölkopf, B., Smola, A. J., & Muller, K. R. (1999).
+Kernel Principal Component Analysis.
+Advances in kernel methods support vector learning, 1327(3), 327-352. MIT Press.
+Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.8744i
+
+A gaussian kernel is used for the processing.
diff --git a/examples/descriptions/modular/preprocessor_laplacianeigemaps.txt b/examples/descriptions/modular/preprocessor_laplacianeigemaps.txt
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+In this example toy data is being processed using Laplacian Eigenmaps
+algorithm as described in
+
+Belkin, M., & Niyogi, P. (2002). 
+Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. 
+Science, 14, 585-591. MIT Press. 
+Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9400&rep=rep1&type=pdf
+
+The number of neighbors for the kNN graph and the heat distribution
+coeffcient is set before processing the data
diff --git a/examples/descriptions/modular/preprocessor_locallylinearembedding.txt b/examples/descriptions/modular/preprocessor_locallylinearembedding.txt
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+In this example toy data is being preprocessed using the Locally Linear Embedding (LLE)
+algorithm as described in
+
+Saul, L. K., Ave, P., Park, F., & Roweis, S. T. (2001).
+An Introduction to Locally Linear Embedding. Available from, 290(5500), 2323-2326.
+Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123.7319&rep=rep1&type=pdf
+
+The number of neighbors used during the linear reconstruction step of the algorithm is set
+before processing of the data.
diff --git a/examples/descriptions/modular/preprocessor_localtangentspacealignment.txt b/examples/descriptions/modular/preprocessor_localtangentspacealignment.txt
diff --git a/...scriptions/modular/preproc_logplusone.txt → ...tions/modular/preprocessor_logplusone.txt b/...scriptions/modular/preproc_logplusone.txt → ...tions/modular/preprocessor_logplusone.txt
diff --git a/examples/descriptions/modular/preprocessor_multidimensionalscaling.txt b/examples/descriptions/modular/preprocessor_multidimensionalscaling.txt
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+In this example toy data is being processed using the multidimensional
+scaling as described on p.261 (Section 12.1) of
+
+Borg, I., & Groenen, P. J. F. (2005).
+Modern multidimensional scaling: Theory and applications. Springer.
+
+Before processing the landmark approximation is disabled.
diff --git a/.../descriptions/modular/preproc_normone.txt → ...riptions/modular/preprocessor_normone.txt b/.../descriptions/modular/preproc_normone.txt → ...riptions/modular/preprocessor_normone.txt
diff --git a/examples/descriptions/modular/preprocessor_pca.txt b/examples/descriptions/modular/preprocessor_pca.txt
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+In this example toy data is being processed using the
+Principal Component Analysis. 
diff --git a/...tions/modular/preproc_prunevarsubmean.txt → .../modular/preprocessor_prunevarsubmean.txt b/...tions/modular/preproc_prunevarsubmean.txt → .../modular/preprocessor_prunevarsubmean.txt
diff --git a/examples/descriptions/modular/preprocessor_randomfouriergausspreproc.txt b/examples/descriptions/modular/preprocessor_randomfouriergausspreproc.txt
diff --git a/...tions/modular/preproc_sortulongstring.txt → .../modular/preprocessor_sortulongstring.txt b/...tions/modular/preproc_sortulongstring.txt → .../modular/preprocessor_sortulongstring.txt
diff --git a/...ptions/modular/preproc_sortwordstring.txt → ...s/modular/preprocessor_sortwordstring.txt b/...ptions/modular/preproc_sortwordstring.txt → ...s/modular/preprocessor_sortwordstring.txt