Integrating structured biological data by Kernel Maximum Mean Discrepancy von Borgwardt, Karsten M., Gretton, Arthur, Rasch, Malte J., Kriegel, Hans-Peter, Schölkopf, Bernhard, Smola, Alex J.

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Estimating the Support of a High-Dimensional Distribution von Schölkopf, Bernhard, Platt, John C., Shawe-Taylor, John, Smola, Alex J., Williamson, Robert C.

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The connection between regularization operators and support vector kernels von Smola, Alex J., Schölkopf, Bernhard, Müller, Klaus-Robert

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Experimentally optimal ν in support vector regression for different noise models and parameter settings von Chalimourda, Athanassia, Schölkopf, Bernhard, Smola, Alex J

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Learning Graph Matching von Caetano, T.S., McAuley, J.J., Li Cheng, Le, Q.V., Smola, A.J.

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Protein function prediction via graph kernels von Borgwardt, Karsten M., Ong, Cheng Soon, Schönauer, Stefan, Vishwanathan, S. V. N., Smola, Alex J., Kriegel, Hans-Peter

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Kernelized Sorting von Quadrianto, N, Smola, A J, Le Song, Tuytelaars, T

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New Support Vector Algorithms von Schölkopf, Bernhard, Smola, Alex J., Williamson, Robert C., Bartlett, Peter L.

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A Generalized Representer Theorem von Schölkopf, Bernhard, Herbrich, Ralf, Smola, Alex J.

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Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators von Williamson, R.C., Smola, A.J., Scholkopf, B.

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A tutorial on support vector regression von Smola, Alex J., Schölkopf, Bernhard

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Experimentally optimal ν in support vector regression for different noise models and parameter settings von Chalimourda, Athanassia, Schölkopf, Bernhard, Smola, Alex J.

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Universal Clustering with Regularization in Probabilistic Space von Nikulin, Vladimir, Smola, Alex J.

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Transductive Gaussian Process Regression with Automatic Model Selection von Le, Quoc V., Smola, Alex J., Gärtner, Thomas, Altun, Yasemin

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Large margin classification for moving targets von KIVINEN, Jyrki, SMOLA, Alex J, WILLIAMSON, Robert C

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Learning Graph Matching von Caetano, Tiberio S., Li Cheng, Le, Quoc V., Smola, Alex J.

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Experimentally optimal v in support vector regression for different noise models and parameter settings von CHALIMOURDA, Athanassia, SCHÖLKOPF, Bernhard, SMOLA, Alex J

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Kernel-dependent support vector error bounds von Scholkopf, B, Shawe-Taylor, J, Smola, A.J, Williamson, R.C

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Regularized Principal Manifolds von Smola, Alex J., Williamson, Robert C., Mika, Sebastian, Schölkopf, Bernhard

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Distributed Flow Algorithms for Scalable Similarity Visualization von Quadrianto, N, Schuurmans, D, Smola, A J

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