Advanced Lectures on Machine Learning

Advanced lectures on machine learning , Advanced lectures on machine learning , کتابخانه دیجیتال جندی شاپور اهواز

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[47]  Yoram Singer,et al.  Leveraged Vector Machines , 1999, NIPS.

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[50]  Tommi S. Jaakkola,et al.  Feature Selection and Dualities in Maximum Entropy Discrimination , 2000, UAI.

[51]  Toniann Pitassi,et al.  A Gradient-Based Boosting Algorithm for Regression Problems , 2000, NIPS.

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[55]  Christopher M. Bishop,et al.  Variational Relevance Vector Machines , 2000, UAI.

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[68]  Gunnar Rätsch,et al.  Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[69]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.

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[78]  John Shawe-Taylor,et al.  A Column Generation Algorithm For Boosting , 2000, ICML.

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[80]  Yoram Singer,et al.  Boosting and Rocchio applied to text filtering , 1998, SIGIR '98.

[81]  G. Rätsch.  Robust Boosting via Convex Optimization , 2001 .

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[83]  Cesare Furlanello,et al.  Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis , 2001, Multiple Classifier Systems.

[84]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[85]  Ron Meir,et al.  Data-Dependent Bounds for Bayesian Mixture Methods , 2002, NIPS.

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[92]  John D. Lafferty,et al.  Boosting and Maximum Likelihood for Exponential Models , 2001, NIPS.

[93]  Ralf Herbrich,et al.  Bayes Point Machines: Estimating the Bayes Point in Kernel Space , 1999 .

[94]  Christopher K. I. Williams.  Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[95]  Yoshua Bengio,et al.  Boosting Neural Networks , 2000, Neural Computation.

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[98]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

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[101]  Gunnar Rätsch,et al.  On the Convergence of Leveraging , 2001, NIPS.

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[104]  Richard Nock,et al.  A Robust Boosting Algorithm , 2002, ECML.

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[107]  Marc Sebban,et al.  Boosting Density Function Estimators , 2002, ECML.

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[113]  Yishay Mansour,et al.  On the boosting ability of top-down decision tree learning algorithms , 1996, STOC '96.

[114]  Manfred K. Warmuth,et al.  Boosting as entropy projection , 1999, COLT '99.

[115]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

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[119]  Terrence J. Sejnowski,et al.  Learning Nonlinear Overcomplete Representations for Efficient Coding , 1997, NIPS.

[120]  Wenxin Jiang,et al.  Some Theoretical Aspects of Boosting in the Presence of Noisy Data , 2001, ICML.

[121]  Manfred K. Warmuth,et al.  The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant , 1995, COLT '95.

[122]  B. Schölkopf,et al.  Linear programs for automatic accuracy control in regression. , 1999 .

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[124]  Gunnar Rätsch,et al.  Robust Ensemble Learning , 2000 .

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[130]  Ran El-Yaniv,et al.  Localized Boosting , 2000, COLT.

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[136]  Klaus-Robert Müller,et al.  Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors , 2002, IEEE Trans. Neural Networks.

[137]  Katya Scheinberg,et al.  A product-form Cholesky factorization method for handling dense columns in interior point methods for linear programming , 2004, Math. Program..

[138]  J. Langford,et al.  FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness , 2000, ICML.

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