Kernel method for clustering based on optimal target vector

TL;DR: A semi-supervised learning estimator which tends to the first kernel principal component as the number of labeled points vanishes is introduced, and a semi- supervised regression and classification algorithm for transductive inference is developed.

TL;DR: In this paper, a semi-supervised learning estimator which tends to the first kernel principal component as the number of labeled points vanishes is introduced, based on the notion of optimal target vector, which is defined as follows.

TL;DR: Conditions explaining how this type of invariance can arise when the multiclass problem is phrased in the context of linear networks are derived are derived.

TL;DR: The testing of four databases indicate that the proposed clustering algorithm can categorise the data numbers in each cluster and find all the cluster centres exactly, and no need priori parameters and stopping criterion correlating to the database.

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.