Literature for talks additional to chapters of the book "The Elements of Statistical Learning":

Decision Trees:

• Peter J. Tan, David L. Dowe and Trevor I. Dix:

Building classification models from microarray data with tree-based classification algorithms

Nearest Neighbor Methods:

• Satoshi Niijima, Satoru Kuhara:

Effective Nearest Neighbor Methods for Multiclass Cancer Classifcation Using Microarray Data

• Trevor Hastie, Robert Tibshirani:

Discriminant Adaptive Nearest Neighbor Classification

Linear Discriminant Analysis:

• Max Welling:

Fisher Linear Discriminant Analysis

Random Forests:

• Tao Shi, David Seligson, Arie S Belldegrun, Aarno Palotie and Steve Horvath:

Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma

Clustering, K-Means:

• N. Bolshakova, F. Azuaje:

Estimating the Number of Clusters in DNA Microarray Data

Principal Component Analysis:

• K. Y. Yeung and W. L. Ruzzo:

Principal component analysis for clustering gene expression data

Variable Selection:

• Eric P. Xing, Michael I. Jordan, Richard M. Karp:

Feature Selection for High-Dimensional Genomic Microarray Data

Dimension Reduction:

• L.J.P. van der Maaten, E.O. Postma, H.J. van den Herik:

Dimensionality Reduction: A Comparative Review