In neural network's Literature, Hebbian learning traditionally refers to the procedure by which Hopfield model and its generalizations store archetypes (i.e., definite patterns that are experienced just once form synaptic matrix). However, term "Learning" in Machine Learning ability of machine extract features from supplied dataset (e.g., made blurred examples these archetypes), order make own ...

Imagine two identical people receive exactly the same training on how to classify certain objects. Perhaps surprisingly, we show that one can then manipulate them into classifying some test items in opposite ways, simply depending on what other test items they are asked to classify (without label feedback). We call this the Test-Item Effect, which can be induced by the order or the distribution...

2 II.Acknowledgment 3 III.Table of

Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic in practice. For practical use, this paper proposes a novel semi-supervised wr...

SOME CONTRIBUTIONS TO SEMI-SUPERVISED LEARNING

We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.