Ingo Steinwart

发表

Support vector machines

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2008, Data Mining and Knowledge Discovery Handbook.

Sparseness of Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2003, J. Mach. Learn. Res..

Learning rates for kernel-based expectile regression

Ingo Steinwart, Muhammad Farooq, Ingo Steinwart, 2018, Machine Learning.

Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms

Ingo Steinwart, Simon Fischer, Ingo Steinwart, 2017, J. Mach. Learn. Res..

Fast Rates for Support Vector Machines

Ingo Steinwart, Clint Scovel, Ingo Steinwart, 2005, COLT.

Which Minimizer Does My Neural Network Converge To? pdf

Ingo Steinwart, Manuel Nonnenmacher, David Reeb, 2020, ArXiv.

Support Vector Machines are Universally Consistent

Ingo Steinwart, Ingo Steinwart, 2002, J. Complex..

A closer look at covering number bounds for Gaussian kernels

Ingo Steinwart, Simon Fischer, Ingo Steinwart, 2019, J. Complex..

Adaptive Density Level Set Clustering

Ingo Steinwart, Ingo Steinwart, 2011, COLT.

Global Minima of DNNs: The Plenty Pantry pdf

Ingo Steinwart, Nicole Mücke, 2019, ArXiv.

An Oracle Inequality for Clipped Regularized Risk Minimizers

Don R. Hush, Ingo Steinwart, Clint Scovel, 2006, NIPS.

Support vector machine

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2013 .

Fast Rates to Bayes for Kernel Machines

Ingo Steinwart, Clint Scovel, Ingo Steinwart, 2004, NIPS.

Fast Learning from Non-i.i.d. Observations

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2009, NIPS.

On the Influence of the Kernel on the Generalization Ability of Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2001 .

Elicitation and Identification of Properties

Siyu Zhang, Ingo Steinwart, Robert C. Williamson, 2014, COLT.

liquidSVM: A Fast and Versatile SVM package pdf

Ingo Steinwart, Philipp Thomann, Ingo Steinwart, 2017, ArXiv.

Support Vector Machines for Forecasting the Evolution of an Unknown Ergodic Dynamical System from Observations with Unknown Noise

Marian Anghel, Ingo Steinwart, Ingo Steinwart, 2022 .

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines

Don R. Hush, Ingo Steinwart, Patrick Kelly, 2006, J. Mach. Learn. Res..

Density Level Detection is Classification

Don R. Hush, Ingo Steinwart, Clint Scovel, 2004, NIPS.

Entropy of convex hulls--some Lorentz norm results

Ingo Steinwart, 2004, J. Approx. Theory.

Universal Kernels on Non-Standard Input Spaces

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2010, NIPS.

Best-scored Random Forest Classification pdf

Xiaoyu Liu, Ingo Steinwart, Hanyuan Hang, 2019, ArXiv.

Forecasting with Historical Data or Process Knowledge under Misspecification: A Comparison

Luke Bornn, Marian Anghel, Ingo Steinwart, 2012 .

Optimal Rates for Regularized Least Squares Regression

Don R. Hush, Ingo Steinwart, Clint Scovel, 2009, COLT.

On Consistency and Robustness of Support Vector Machines

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2009 .

A Sober Look at Neural Network Initializations pdf

Ingo Steinwart, Ingo Steinwart, 2019, ArXiv.

Optimal Learning Rates for Localized SVMs

Ingo Steinwart, Mona Meister, Ingo Steinwart, 2015, J. Mach. Learn. Res..

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent pdf

Ingo Steinwart, David Holzmüller, Ingo Steinwart, 2020, J. Mach. Learn. Res..

Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds

Ingo Steinwart, Ingo Steinwart, 2003, NIPS.

Consistency and Rates for Clustering with DBSCAN

Ingo Steinwart, Bharath K. Sriperumbudur, Ingo Steinwart, 2012, AISTATS.

Sparsity of SVMs that use the epsilon-insensitive loss

Andreas Christmann, Ingo Steinwart, 2008, NIPS.

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels

Don R. Hush, Ingo Steinwart, Clint Scovel, 2006, IEEE Transactions on Information Theory.

Optimal learning rates for least squares SVMs using Gaussian kernels

Ingo Steinwart, Mona Eberts, Ingo Steinwart, 2011, NIPS.

Towards an axiomatic approach to hierarchical clustering of measures pdf

Nico Schmid, Ingo Steinwart, Philipp Thomann, 2015, Journal of machine learning research.

A short note on the comparison of interpolation widths, entropy numbers, and Kolmogorov widths

Ingo Steinwart, Ingo Steinwart, 2016, J. Approx. Theory.

Oracle inequalities for support vector machines that are based on random entropy numbers

Ingo Steinwart, Ingo Steinwart, 2009, J. Complex..

How SVMs can estimate quantiles and the median

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2007, NIPS.

Learning from dependent observations

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, J. Multivar. Anal..

Robust learning from bites for data mining

Andreas Christmann, Mia Hubert, Ingo Steinwart, 2007, Comput. Stat. Data Anal..

On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2004, J. Mach. Learn. Res..

Function Classes That Approximate the Bayes Risk

Don R. Hush, Ingo Steinwart, Clint Scovel, 2006, COLT.

Gaps in Support Vector Optimization

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, COLT.

Measuring the Capacity of Sets of Functions in the Analysis of ERM

Ingo Steinwart, Ingo Steinwart, 2015 .

A Classification Framework for Anomaly Detection

Don R. Hush, Ingo Steinwart, Clint Scovel, 2005, J. Mach. Learn. Res..

Some Remarks on the Statistical Analysis of SVMs and Related Methods

Ingo Steinwart, Ingo Steinwart, 2013, Empirical Inference.

On the Influence of the Kernel on the Consistency of Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2002, J. Mach. Learn. Res..

Optimal Learning with Anisotropic Gaussian SVMs pdf

Ingo Steinwart, Hanyuan Hang, Ingo Steinwart, 2018, ArXiv.

Training SVMs Without Offset

Don R. Hush, Ingo Steinwart, Clint Scovel, 2011, J. Mach. Learn. Res..

Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space pdf

Ingo Steinwart, 2020, ArXiv.

Fast learning from α-mixing observations

Ingo Steinwart, Hanyuan Hang, Ingo Steinwart, 2014, J. Multivar. Anal..

Learning with Hierarchical Gaussian Kernels pdf

Nico Schmid, Ingo Steinwart, Philipp Thomann, 2016, ArXiv.

Kernel Density Estimation for Dynamical Systems

Johan A. K. Suykens, Yunlong Feng, Ingo Steinwart, 2016, J. Mach. Learn. Res..

Learning Theory Estimates with Observations from General Stationary Stochastic Processes

Johan A. K. Suykens, Yunlong Feng, Ingo Steinwart, 2016, Neural Computation.

Stability of Unstable Learning Algorithms

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, Machine Learning.

Spatial Decompositions for Large Scale SVMs pdf

Ingo Steinwart, Mona Meister, Philipp Thomann, 2017, AISTATS.

Radial kernels and their reproducing kernel Hilbert spaces

Don R. Hush, James Theiler, Ingo Steinwart, 2010, J. Complex..

Consistency of support vector machines and other regularized kernel classifiers

Ingo Steinwart, Ingo Steinwart, 2005, IEEE Transactions on Information Theory.

Using support vector machines for anomalous change detection

Daniel Llamocca, James Theiler, Ingo Steinwart, 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

On the Optimal Parameter Choice for v-Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning pdf

Ingo Steinwart, Manuel Nonnenmacher, David Reeb, 2021, ArXiv.

Adaptive learning rates for support vector machines working on data with low intrinsic dimension

Ingo Steinwart, Thomas Hamm, Ingo Steinwart, 2020, The Annals of Statistics.

Intrinsic Dimension Adaptive Partitioning for Kernel Methods

Ingo Steinwart, Thomas Hamm, Ingo Steinwart, 2021, SIAM Journal on Mathematics of Data Science.

A closer look at covering number bounds for Gaussian kernels

Ingo Steinwart, Simon Fischer, 2021, J. Complex..

On the Existence of Continuous Modifications of Vector-Valued Random Fields

Ingo Steinwart, Ingo Steinwart, Katrin Mittmann, 2003 .

Which data-dependent bounds are suitable for SVM ’ s ? ⋆

Ingo Steinwart, Ingo Steinwart, 2010 .

Consistency of kernel-based quantile regression

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2008 .

Convergence Types and Rates in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties

Ingo Steinwart, Ingo Steinwart, 2014, Potential Analysis.

Adaptive Clustering Using Kernel Density Estimators

Ingo Steinwart, Bharath K. Sriperumbudur, Ingo Steinwart, 2017, J. Mach. Learn. Res..

Hypothesis Testing for Validation and Certification

Ingo Steinwart, Clint Scovel, Ingo Steinwart, 2013, 1302.6427.

Some Estimates for the Entropy Numbers of Convex Hulls With Finitely Many Extreme Points

Ingo Steinwart, 1999 .