Bouligand Derivatives and Robustness of Support Vector Machines for Regression
The Journal of Machine Learning Research
Fast evolutionary maximum margin clustering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Oracle inequalities for support vector machines that are based on random entropy numbers
Journal of Complexity
Robustness of reweighted Least Squares Kernel Based Regression
Journal of Multivariate Analysis
Using an Information Quality Framework to Evaluate the Quality of Product Reviews
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Robustness and Regularization of Support Vector Machines
The Journal of Machine Learning Research
Density-based similarity measures for content based search
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Using physiological signals to detect natural interactive behavior
Applied Intelligence
Hilbert Space Embeddings and Metrics on Probability Measures
The Journal of Machine Learning Research
Rademacher chaos complexities for learning the kernel problem
Neural Computation
Efficient statement identification for automatic market forecasting
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Radial kernels and their reproducing kernel Hilbert spaces
Journal of Complexity
Bubbles detection on sea surface images
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Optimal learning rates for least squares regularized regression with unbounded sampling
Journal of Complexity
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On qualitative robustness of support vector machines
Journal of Multivariate Analysis
Support Vector Machines with the Ramp Loss and the Hard Margin Loss
Operations Research
Covering numbers of Gaussian reproducing kernel Hilbert spaces
Journal of Complexity
Weighted Markov chain model for musical composer identification
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Full length article: Concentration estimates for the moving least-square method in learning theory
Journal of Approximation Theory
Information, Divergence and Risk for Binary Experiments
The Journal of Machine Learning Research
Universality, Characteristic Kernels and RKHS Embedding of Measures
The Journal of Machine Learning Research
Automatic identification approach for sea surface bubbles detection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Online anomaly detection in unmanned vehicles
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A multimodal people recognition system for an intelligent environment
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Speedy local search for semi-supervised regularized least-squares
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
The structured elastic net for quantile regression and support vector classification
Statistics and Computing
Asymptotic normality of support vector machine variants and other regularized kernel methods
Journal of Multivariate Analysis
Consistency and asymptotic normality of FastICA and bootstrap FastICA
Signal Processing
The Journal of Machine Learning Research
An overview of the use of neural networks for data mining tasks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Mathematical and Computer Modelling: An International Journal
Stable Evaluation of Gaussian Radial Basis Function Interpolants
SIAM Journal on Scientific Computing
On Dimension-independent Rates of Convergence for Function Approximation with Gaussian Kernels
SIAM Journal on Numerical Analysis
Learning how to trade off aesthetic criteria in layout
Proceedings of the 2012 ACM symposium on Document engineering
X-Class: Associative Classification of XML Documents by Structure
ACM Transactions on Information Systems (TOIS)
Towards partners profiling in human robot interaction contexts
SIMPAR'12 Proceedings of the Third international conference on Simulation, Modeling, and Programming for Autonomous Robots
Tighter PAC-Bayes bounds through distribution-dependent priors
Theoretical Computer Science
Fast learning rates for sparse quantile regression problem
Neurocomputing
Towards non-linear constraint estimation for expensive optimization
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Learning nonlinear hybrid systems: from sparse optimization to support vector regression
Proceedings of the 16th international conference on Hybrid systems: computation and control
Advances in Computational Mathematics
Multiple spectral kernel learning and a gaussian complexity computation
Neural Computation
Universal consistency of localized versions of regularized kernel methods
The Journal of Machine Learning Research
On the convergence rate of lp-norm multiple kernel learning
The Journal of Machine Learning Research
Robust kernel density estimation
The Journal of Machine Learning Research
Eigenvalues perturbation of integral operator for kernel selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Neural Computation
Journal of Multivariate Analysis
Whose and what chatter matters? The effect of tweets on movie sales
Decision Support Systems
Towards automated appliance recognition using an EMF sensor in NILM platforms
Advanced Engineering Informatics
Conjugate relation between loss functions and uncertainty sets in classification problems
The Journal of Machine Learning Research
Nonparametric sparsity and regularization
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Signal peptide discrimination and cleavage site identification using SVM and NN
Computers in Biology and Medicine
Artificial Intelligence in Medicine
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This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and their computational efficiency compared to several other methods. Since their appearance in the early nineties, support vector machines and related kernel-based methods have been successfully applied in diverse fields of application such as bioinformatics, fraud detection, construction of insurance tariffs, direct marketing, and data and text mining. As a consequence, SVMs now play an important role in statistical machine learning and are used not only by statisticians, mathematicians, and computer scientists, but also by engineers and data analysts. The book provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature. The book can thus serve as both a basis for graduate courses and an introduction for statisticians, mathematicians, and computer scientists. It further provides a valuable reference for researchers working in the field. The book covers all important topics concerning support vector machines such as: loss functions and their role in the learning process; reproducing kernel Hilbert spaces and their properties; a thorough statistical analysis that uses both traditional uniform bounds and more advanced localized techniques based on Rademacher averages and Talagrand's inequality; a detailed treatment of classification and regression; a detailed robustness analysis; and a description of some of the most recent implementation techniques. To make the book self-contained, an extensive appendix is added which provides the reader with the necessary background from statistics, probability theory, functional analysis, convex analysis, and topology.