A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Generalization performance of support vector machines and other pattern classifiers
Advances in kernel methods
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Kernel principal component analysis
Advances in kernel methods
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Representing Functional Data Using Support Vector Machines
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Transferred Dimensionality Reduction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
RV-SVM: An Efficient Method for Learning Ranking SVM
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Combining Functional Data Projections for Time Series Classification
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
RankSVR: can preference data help regression?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
An efficient EM-ICP algorithm for symmetric consistent non-linear registration of point sets
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
On Learning and Cross-Validation with Decomposed Nyström Approximation of Kernel Matrix
Neural Processing Letters
Least square regression with lp-coefficient regularization
Neural Computation
Learning Multi-modal Similarity
The Journal of Machine Learning Research
Stackelberg games for adversarial prediction problems
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate kernel k-means: solution to large scale kernel clustering
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Universality, Characteristic Kernels and RKHS Embedding of Measures
The Journal of Machine Learning Research
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Using landmarks as a deformation prior for hybrid image registration
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Speedy local search for semi-supervised regularized least-squares
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Modeling social strength in social media community via kernel-based learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Estimating predictive variances with kernel ridge regression
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Gene selection of DNA microarray data based on regularization networks
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Locality-convolution kernel and its application to dependency parse ranking
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Regularized least-squares for parse ranking
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Bayesian kernel learning methods for parametric accelerated life survival analysis
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
An online framework for learning novel concepts over multiple cues
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Metric Learning for Estimating Psychological Similarities
ACM Transactions on Intelligent Systems and Technology (TIST)
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
Regularized learning in Banach spaces as an optimization problem: representer theorems
Journal of Global Optimization
Subspace regularized linear discriminant analysis for small sample size problems
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Structured apprenticeship learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Kernelization of matrix updates, when and how?
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Accelerated max-margin multiple kernel learning
Applied Intelligence
A sparse kernel algorithm for online time series data prediction
Expert Systems with Applications: An International Journal
Online multi-modal distance learning for scalable multimedia retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
An identity for kernel ridge regression
Theoretical Computer Science
Vector-valued reproducing kernel Banach spaces with applications to multi-task learning
Journal of Complexity
Learning nonlinear hybrid systems: from sparse optimization to support vector regression
Proceedings of the 16th international conference on Hybrid systems: computation and control
Learning with boundary conditions
Neural Computation
Spatio-temporal fisher vector coding for surveillance event detection
Proceedings of the 21st ACM international conference on Multimedia
Robust kernel density estimation
The Journal of Machine Learning Research
Static prediction games for adversarial learning problems
The Journal of Machine Learning Research
Consistent identification of Wiener systems: A machine learning viewpoint
Automatica (Journal of IFAC)
Large scale online kernel classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Similar handwritten Chinese character recognition by kernel discriminative locality alignment
Pattern Recognition Letters
Laplacian minimax probability machine
Pattern Recognition Letters
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
The Journal of Machine Learning Research
Premise Selection for Mathematics by Corpus Analysis and Kernel Methods
Journal of Automated Reasoning
Training sparse SVM on the core sets of fitting-planes
Neurocomputing
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
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Wahba's classical representer theorem states that the solutions of certain risk minimization problems involving an empirical risk term and a quadratic regularizer can be written as expansions in terms of the training examples. We generalize the theorem to a larger class of regularizers and empirical risk terms, and give a self-contained proof utilizing the feature space associated with a kernel. The result shows that a wide range of problems have optimal solutions that live in the finite dimensional span of the training examples mapped into feature space, thus enabling us to carry out kernel algorithms independent of the (potentially infinite) dimensionality of the feature space.