Download Advances in Neural Information Processing Systems 19: by Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann PDF

By Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)

The yearly Neural details Processing platforms (NIPS) convention is the flagship assembly on neural computation and computing device studying. It attracts a various crew of attendees—physicists, neuroscientists, mathematicians, statisticians, and computing device scientists—interested in theoretical and utilized features of modeling, simulating, and development neural-like or clever structures. The displays are interdisciplinary, with contributions in algorithms, studying concept, cognitive technological know-how, neuroscience, mind imaging, imaginative and prescient, speech and sign processing, reinforcement studying, and purposes. simply twenty-five percentage of the papers submitted are authorised for presentation at NIPS, so the standard is outstandingly excessive. This quantity comprises the papers provided on the December 2006 assembly, held in Vancouver.

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Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In Proc. of the 21st Intl. Conference on Machine Learning, 2004. [5] Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119–139, August 1997. [6] S. Shalev-Shwartz and Y. Singer. Online learning meets optimization in the dual. In Proc. of the Nineteenth Annual Conference on Computational Learning Theory, 2006.

In order to evaluate the predictive capabilities of the Prior PAC-Bayes bound as a means to select models with low test error rate, Table 4 displays the averaged test error corresponding to the models selected in the previous experiment (note that in this case the computational burden involved in determining the model is increased by the training of the SVM that learns the prior wr ). Table 5 displays the test error rate obtained by SVMs with their hyperparameters tuned on the above mentioned grid by means of ten-fold cross-validation, that serves as a baseline method for comparison purposes.

Each instance is a vector in Rn+k−1 . The first n entries of the vector are set to be the elements of x, the remaining k − 1 entries are set to −δi,j . That is, the i’th entry in the j’th vector is set to −1 if i = j and to 0 otherwise. The label of the first y − 1 instances is 1, while the remaining k − y instances are labeled as −1. Once we learned an expanded vector in Rn+k−1 , the regressor ω is obtained by taking the first n components of the expanded vector and the thresholds b1 , . . , bk−1 are set to be the last k − 1 elements.

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