Titlebook: Artificial Neural Networks and Machine Learning- ICANN 2011; 21st International C Timo Honkela,W?odzis?aw Duch,Samuel Kaski Conference proc

干旱

Reformulations, Consequences, and Criteria,idal clusters in Euclidean space. Kernel methods extend these approaches to more complex cluster forms, and they have been recently integrated into several clustering techniques. While leading to very flexible representations, kernel clustering has the drawback of high memory and time complexity due

乏味

palliate

Fermat’s Last Theorem for Amateurs, and . the average number of non-zero features per example. The method generalizes the fastest previously known approach, which achieves the same efficiency only in restricted special cases. The excellent scalability of the proposed method is demonstrated experimentally.

伴隨而來

Transformation Equivariant Boltzmann Machines,ibes the selection of the transformed view of the canonical connection weights associated with the unit. This enables the inferences of the model to transform in response to transformed input data in a . way, and avoids learning multiple features differing only with respect to the set of transformat

屈尊

Hirsutism

A Hierarchical Generative Model of Recurrent Object-Based Attention in the Visual Cortex,object-based attention, combining generative principles with attentional ones. We show: (1) How inference in DBMs can be related qualitatively to theories of attentional recurrent processing in the visual cortex; (2) that deepness and topographic receptive fields are important for realizing the atte

死貓他燒焦

,?1-Penalized Linear Mixed-Effects Models for BCI,so methods. We apply this ?.-penalized linear regression mixed-effects model to a large scale real world problem: by exploiting a large set of brain computer interface data we are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifyi

的是兄弟

indenture

Transforming Auto-Encoders,puts. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a whole vector of outputs including an explicit representation of the pose of the feature. We show how neural networks can be used to learn features that output a whole vector of

debble