Kernel methods in machine learning
The Annals of StatisticsPublished 26 May 2008Open access
Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola
Citations1,548
SJR quartileQ1
SJR score4.77
SNIP3.13
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A review of machine learning methods employing positive definite kernels, ranging from binary classifiers to sophisticated methods for estimation with structured data, which include nonlinear functions as well as functions defined on nonvectorial data.
Abstract
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data.\n¶\nWe cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.
Keywords
Computer ScienceEngineering
Journal of the Royal Statistical Society Series B (Statistical Methodology)Regression Shrinkage and Selection Via the Lasso
51,790 Citations1996Robert Tibshirani
A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Machine LearningSupport-vector networks
40,463 Citations1995Corinna Cortes, Vladimir Vapnik
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
The Nature of Statistical Learning Theory
39,279 Citations1995Vladimir Vapnik
A training algorithm for optimal margin classifiers
11,594 Citations1992Bernhard E. Boser, Isabelle Guyon +1 more
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
TechnometricsRidge Regression: Biased Estimation for Nonorthogonal Problems
8,927 Citations1970Arthur E. Hoerl, Robert W. Kennard
Neural ComputationNonlinear Component Analysis as a Kernel Eigenvalue Problem
8,078 Citations1998Bernhard Schölkopf, Alexander J. Smola +1 more
A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Experiments with a new boosting algorithm
7,585 Citations1996Yoav Freund, Robert E. Schapire
SIAM Journal on Scientific ComputingAtomic Decomposition by Basis Pursuit
6,899 Citations1998Scott Shaobing Chen, David L. Donoho +1 more
Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
Cambridge University Press eBooksKernel Methods for Pattern Analysis
6,598 Citations2004John Shawe‐Taylor, Nello Cristianini
This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Neural ComputationEstimating the Support of a High-Dimensional Distribution
5,946 Citations2001Bernhard Schölkopf, John Platt +3 more
The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
The MIT Press eBooksFast Training of Support Vector Machines Using Sequential Minimal Optimization
5,462 Citations1998John Platt
Proceedings of the IEEENetworks for approximation and learning
3,257 Citations1990Tomaso Poggio, Federico Girosi
Regularization theory and a theoretical framework for approximation that leads to a class of three-layer networks called regularization networks are discussed, generalized to a formulation that includes task-dependent clustering and dimensionality reduction.
now publishers, Inc. eBooksGraphical Models, Exponential Families, and Variational Inference
3,154 Citations2007Martin J. Wainwright, Michael I. Jordan
Neural ComputationNew Support Vector Algorithms
2,816 Citations2000Bernhard Schölkopf, Alex Smola +2 more
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing
2,702 Citations1996Vladimir Vapnik, Steven E. Golowich +1 more
This presentation reports results of applying the Support Vector method to problems of estimating regressions, constructing multidimensional splines, and solving linear operator equations.
MPG.PuRe (Max Planck Society)Large Margin Methods for Structured and Interdependent Output Variables
1,952 Citations2005Ioannis Tsochantaridis, Thorsten Joachims +2 more
This paper proposes to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation and presents a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems.
The MIT Press eBooksAdvances in Large-Margin Classifiers
1,870 Citations2000
This book provides an overview of recent developments in large margin classifiers, examines connections with other methods, and identifies strengths and weaknesses of the method, as well as directions for future research.
Proceedings of the IEEEBlind signal separation: statistical principles
1,852 Citations1998J.-F. Cardoso
The objectives of this paper are to review some of the approaches that have been developed to address blind signal separation and independent component analysis, to illustrate how they stem from basic principles, and to show how they relate to each other.
On the algorithmic implementation of multiclass kernel-based vector machines
1,790 Citations2002Koby Crammer, Yoram Singer
This paper describes the algorithmic implementation of multiclass kernel-based vector machines using a generalized notion of the margin to multiclass problems, and describes an efficient fixed-point algorithm for solving the reduced optimization problems and proves its convergence.
BioinformaticsIntegrating structured biological data by Kernel Maximum Mean Discrepancy
1,710 Citations2006Karsten Borgwardt, Arthur Gretton +4 more
A novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by the experiments.
IEEE Transactions on ComputersA Projection Pursuit Algorithm for Exploratory Data Analysis
1,635 Citations1974Jerome H. Friedman, John W. Tukey
Lecture notes in computer scienceMeasuring Statistical Dependence with Hilbert-Schmidt Norms
1,526 Citations2005Arthur Gretton, Olivier Bousquet +2 more
An independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator, or HSIC, is proposed.
Journal of Mathematical PsychologyThe area above the ordinal dominance graph and the area below the receiver operating characteristic graph
1,442 Citations1975Donald Bamber
Methods for Solving Incorrectly Posed Problems
1,326 Citations1984Vladimir A. Morozov
A new book enPDFd methods for solving incorrectly posed problems that can be a new way to explore the knowledge and get one thing to always remember in every reading time, even step by step is shown.
Journal of Mathematical Analysis and ApplicationsSome results on Tchebycheffian spline functions
1,255 Citations1971George Kimeldorf, Grace Wahba
Shallow parsing with conditional random fields
1,251 Citations2003Fei Sha, Fernando Pereira
This work shows how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model.
The Annals of Mathematical StatisticsGeneralized Iterative Scaling for Log-Linear Models
1,203 Citations1972J. N. Darroch, D. Ratcliff
Graduate texts in mathematicsHarmonic Analysis on Semigroups
1,125 Citations1984Christian Berg, Jens Peter Christensen +1 more
The MIT Press eBooksKernel Methods in Computational Biology
1,034 Citations2004Jean‐Philippe Vert, 阿久津, 達也
This book presents different ideas for the design of kernel functions specifically adapted to various biological data, and covers different approaches to learning from heterogeneous data.
Journal of the American Statistical AssociationConvexity, Classification, and Risk Bounds
1,019 Citations2006Peter L. Bartlett, Michael I. Jordan +1 more
A general quantitative relationship between the risk as assessed using the 0–1 loss and the riskAs assessed using any nonnegative surrogate loss function is provided, and it is shown that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function.
SIAM ReviewSemi-Infinite Programming: Theory, Methods, and Applications
1,005 Citations1993R. Hettich, K. O. Kortanek
This paper treats numerical methods based on either discretization or local reduction with the emphasis on the design of superlinearly convergent (SQP-type) methods.
THE SPECTRUM KERNEL: A STRING KERNEL FOR SVM PROTEIN CLASSIFICATION
944 Citations2001Christina S. Leslie, Eleazar Eskin +1 more
A new sequence-similarity kernel, the spectrum kernel, is introduced for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem and performs well in comparison with state-of-the-art methods for homology detection.
Lecture notes in computer scienceKernels and Regularization on Graphs
839 Citations2003Alexander J. Smola, Risi Kondor
It is shown that the class of positive, monotonically decreasing functions on the unit interval leads to kernels and corresponding regularization operators and can be found as a special case of the reasoning.
The MIT Press eBooksConvolution Kernels for Natural Language
812 Citations2002Michael J. Collins, Nigel Duffy
It is shown how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and experimental results on the ATIS corpus of parse trees are given.
A support vector method for multivariate performance measures
800 Citations2005Thorsten Joachims
An algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea and all measures that can be computed from the contingency table are given.
arXiv (Cornell University)Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
791 Citations2012Luke Zettlemoyer, Michael J. Collins
Optimization methods & softwareRobust linear programming discrimination of two linearly inseparable sets
784 Citations1992Kristin P. Bennett, O. L. Mangasarian
A single linear programming formulation is proposed which generates a plane that of minimizes an average sum of misclassified points belonging to two disjoint points sets in n-dimensional real space, without the imposition of extraneous normalization constraints that inevitably fail to handle certain cases.
Marginalized kernels between labeled graphs
685 Citations2003Hisashi Kashima, Koji Tsuda +1 more
A new kernel function between two labeled graphs that is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming and presents promising empirical results in classification of chemical compounds.
Applied Physics Letters10.1162/153244303768966085
622 Citations2000
A class of algorithms for independent component analysis which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space is presented, showing that these algorithms outperform many of the presently known algorithms.
Neural NetworksThe connection between regularization operators and support vector kernels
621 Citations1998Alex Smola, Bernhard Schölkopf +1 more
It is proved that the Green's Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties and it is shown that a large number of radial basis functions, namely conditionally positive definite functions, may be used as support vector kernel.
Machine LearningTraining Invariant Support Vector Machines
582 Citations2002Dennis DeCoste, Bernhard Schölkopf
A kernel view of the dimensionality reduction of manifolds
543 Citations2004Jihun Ham, Daniel D. Lee +2 more
Isomap, graph Laplacian eigenmap, and locally linear embedding all utilize local neighborhood information to construct a global embedding of the manifold and it is shown how all three algorithms can be described as kernel PCA on specially constructed Gram matrices.
Applied Physics Letters10.1162/15324430152733133
506 Citations2000
A general method for combining the classifiers generated on the binary problems is proposed, and a general empirical multiclass loss bound is proved given the empirical loss of the individual binary learning algorithms.
Proceedings of the Royal Society of London Series A Containing Papers of a Mathematical and Physical CharacterFunctions of positive and negative type, and their connection with the theory of integral equations
486 Citations1909James W. Mercer
Transactions of the American Mathematical SocietyTheory of Reproducing Kernels
483 Citations1950N. Aronszajn
Hidden Markov support vector machines
469 Citations2003Yasemin Altün, Ioannis Tsochantaridis +1 more
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which it is called HM-SVMs and handles dependencies between neighboring labels using Viterbi decoding.
Teubner-Archiv zur MathematikGrundzüge einer allgemeinen Theorie der linearen Integralgleichungen
451 Citations1989David Hilbert
ACM SIGKDD Explorations NewsletterA survey of kernels for structured data
450 Citations2003Thomas Gärtner
This survey describes several approaches of defining positive definite kernels on structured instances directly on the basis of areal vector space and thus in a single table.
International Conference on Machine LearningDiffusion kernels on graphs and other discrete structures
428 Citations2002Risi Kondor
Histological preparations from 120 cases of tubal pregnancy were reviewed and forty-five cases were found suitable for nuclear sexing; in these the chorionic villi contained a sufficient number of nuclei in a sufficient state of preservation to show the sex chromatin if present.
BioinformaticsEngineering support vector machine kernels that recognize translation initiation sites
425 Citations2000Alexander Zien, Gunnar Rätsch +4 more
With the described techniques the recognition performance can be improved by 26% over leading existing approaches, and there is evidence that existing related methods could profit from advanced TIS recognition.
ESAIM Probability and StatisticsTheory of Classification: a Survey of Some Recent Advances
416 Citations2005Stéphane Boucheron, Olivier Bousquet +1 more
Computational LinguisticsDiscriminative Reranking for Natural Language Parsing
407 Citations2005Michael J. Collins, Terry Koo
The boosting approach to ranking problems described in Freund et al. (1998) is applied to parsing the Wall Street Journal treebank, and it is argued that the method is an appealing alternative-in terms of both simplicity and efficiency-to work on feature selection methods within log-linear (maximum-entropy) models.
IEEE Transactions on Information TheoryRademacher penalties and structural risk minimization
371 Citations2001Vladimir Koltchinskii
This work suggests a penalty function to be used in various problems of structural risk minimization, based on the sup-norm of the so-called Rademacher process indexed by the underlying class of functions (sets), and obtains oracle inequalities for the theoretical risk of estimators, obtained by structural minimization of the empirical risk withRademacher penalties.
Unifying collaborative and content-based filtering
337 Citations2004Justin Basilico, Thomas Hofmann
This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function.
Operations ResearchLinear and Nonlinear Separation of Patterns by Linear Programming
330 Citations1965O. L. Mangasarian
In this work it is shown that both linear and nonlinear separation may be achieved by linear programming.
Neural ComputationAdaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information
313 Citations1997Howard H. Yang, Шун-ичи Амари
It is shown by computer simulation that the convergence of the stochastic descent algorithms is improved by using the natural gradient and the adaptively estimated cumulants.
IEEE Transactions on Pattern Analysis and Machine IntelligenceIterative kernel principal component analysis for image modeling
310 Citations2005Kwa, Matthias Franz +1 more
A new iterative method for performing KPCA is proposed, the kernel Hebbian algorithm, which iteratively estimates the kernel principal components with only linear order memory complexity.
AlgorithmicaOn a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion
297 Citations1998Alexander J. Smola, Bernhard Schölkopf
A kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion is presented, adopting a regularization-theoretic framework.
The MIT Press eBooksFast Kernels for String and Tree Matching
293 Citations2004S. V. N. Vishwanathan, Alexander J. Smola
A new algorithm suitable for matching discrete objects such as strings and trees in linear time is presented, thus obviating dynamic programming with quadratic time complexity and improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.
The MIT Press eBooksDynamic Alignment Kernels
242 Citations2000Chris Watkins
This paper shows that the scores produced by certain dynamic align ment algorithms for sequences are in fact valid kernel functions, proved by expressing the alignment scores explicitly as dot products.
Statistics and ComputingApplications of a general propagation algorithm for probabilistic expert systems
238 Citations1992A. P. Dawid
This paper analyses a ‘flow-propagation’ algorithm for calculating marginal and conditional distributions in a probabilistic expert system in detail, and shows how it can be modified to perform other tasks, including maximization of the joint density and simultaneous 'fast retraction' of evidence entered on several variables.
The Annals of StatisticsSmoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy : the 1994 Neyman Memorial Lecture
237 Citations1995Grace Wahba, Yuedong Wang +3 more
The overall scheme is applied to Bernoulli data from the Wisconsin Epidemiologic Study of Diabetic Retinopathy to model the risk of progression of diabetic retinopathy as a function of glycosylated hemoglobin, duration of diabetes and body mass index.
Applied Physics Letters10.1162/153244302760185252
225 Citations2000
It is shown that the soft margin algorithms with universal kernels are consistent for a large class of classification problems including some kind of noisy tasks provided that the regularization parameter is chosen well.
Probabilistic Kernel Regression Models
207 Citations1999Tommi Jaakkola, David Haussler
A class of exible conditional probability models and techniques for classi cation regression problems that comes from the use of kernel functions as in support vector machines and the generality from dual formulations of stan dard regression models is introduced.
Journal of the American Statistical AssociationExploratory Projection Pursuit
185 Citations1987Jerome H. Friedman
Journal of ComplexitySupport Vector Machines are Universally Consistent
167 Citations2002Ingo Steinwart
It is shown that the 1-norm soft margin classifier with Gaussian RBF kernel on a compact subset X of Rd and regularization parameter cn = nβ-1 is universally consistent, if n is the training set size and 0 >β> 1/d.
Lecture notes in computer scienceA Few Notes on Statistical Learning Theory
163 Citations2003Shahar Mendelson
The focus in this article is on the theoretical side and not on the applicative one; hence, it shall not present examples which may be interesting from the practical point of view but have little theoretical significance.
Biological CyberneticsOn optimal nonlinear associative recall
139 Citations1975Tomaso Poggio
The problem of determining the nonlinear function (“blackbox”) which optimally associates two sets of data is considered and an iteration method based on the concept of the generalized inverse of a matrix provides the polynomial mapping of degreek onX by whichY is retrieved in an optimal way in the least squares sense.
Lecture notes in computer scienceBhattacharyya and Expected Likelihood Kernels
127 Citations2003Tony Jebara, Risi Kondor
A new class of kernels between distributions that permits discriminative estimation via, for instance, support vector machines, while exploiting the properties, assumptions, and invariances inherent in the choice of generative model are introduced.
International Journal of Computer VisionBinet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes
127 Citations2006S. V. N. Vishwanathan, Alexander J. Smola +1 more
The derivation provides a unifying framework for all kernels on dynamical systems currently used in machine learning, including kernels derived from the behavioral framework, diffusion processes, marginalized kernels, kernels on graphs, and the kernels on sets arising from the subspace angle approach.
The MIT Press eBooksSupport Vector Regression with ANOVA Decomposition Kernels
126 Citations1998Mark O. Stitson, Alex Gammerman +4 more
Support Vector Machines using ANOVA Decomposition Kernels (SVAD) is used with spline kernels and results show that SVAD performs better than the respective non ANOVA decomposition kernel.
Kernel conditional random fields
124 Citations2004John Lafferty, Xiaojin Zhu +1 more
Kernel conditional random fields are introduced as a framework for discriminative modeling of graph-structured data and a procedure for greedily selecting cliques in the dual representation is proposed, which allows sparse representations.
Lecture notes in statisticsSemi-parametric Generalized Linear Models
124 Citations1985Peter J. Green, Brian S. Yandell
A Conditional Random Field for Discriminatively-Trained Finite-State String Edit Distance
114 Citations2005Andrew McCallum, Kedar Bellare +1 more
This paper presents discriminative string-edit CRFs, a finite-state conditional random field model for edit sequences between strings, trained on both positive and negative instances of string pairs.
The Annals of StatisticsGeneralized Quantile Processes
100 Citations1992J.H.J. Einmahl, David M. Mason
STATISTICAL INFERENCE ON TIME SERIES BY RKHS METHODS.
91 Citations1970Emanuel Parzen
The aim of this review is to outline the unifying role of reproducing kernel Hilbert spaces (RKHS) in the theory of time series.
A general regression technique for learning transductions
84 Citations2005Corinna Cortes, Mehryar Mohri +1 more
A novel and conceptually cleaner formulation of kernel dependency estimation provides a simple framework for estimating the regression coefficients, and an efficient algorithm for computing the pre-image from the regression coefficient extends the applicability of Kernel dependency estimation to output sequences.
PLoS Computational BiologyImproving the Caenorhabditis elegans Genome Annotation Using Machine Learning
77 Citations2007Gunnar Rätsch, Sören Sonnenburg +5 more
The state-of-the-art machine learning methods employed to assay and improve the accuracy of the genome annotation of the nematode Caenorhabditis elegans are concluded to be greatly enhanced using modern machine learning technology.
IEEE Transactions on Signal ProcessingConsistent independent component analysis and prewhitening
64 Citations2005Aiyou Chen, Peter J. Bickel
The validity of prewhitening is established: a preprocessing technique used by many ICA algorithms, as applied to the CHFICA method, and the surprising effectiveness of this technique even when some components have heavy tails and others do not is established.
Journal of the American Statistical AssociationAutomatic Smoothing of Regression Functions in Generalized Linear Models
58 Citations1986Finbarr O’Sullivan, Brian S. Yandell +1 more
Gaussian process classification for segmenting and annotating sequences
53 Citations2004Yasemin Altün, Thomas Hofmann +1 more
Gaussian Process classification is generalized to predict multiple labels by taking dependencies between neighboring labels into account, motivated by the desire to retain rigorous probabilistic semantics, while overcoming limitations of parametric methods like Conditional Random Fields.
Scale-sensitive dimensions, uniform convergence, and learnability
52 Citations2002Noga Alon, Shalev Ben-David +2 more
This paper proves, through a generalization of Sauer's lemma that may be interesting in its own right, a new characterization of uniform Glivenko-Cantelli classes, the first based on a simple combinatorial quantity generalizing the Vapnik-Chervonenkis dimension.
Linear Algebra and its ApplicationsRestricted canonical correlations
51 Citations1994Shubhabrata Das, Pranab Kumar Sen
Kernel Constrained Covariance for Dependence Measurement
48 Citations2005Arthur Gretton, Alexander J. Smola +8 more
A Machine Learning Approach to Conjoint Analysis
48 Citations2004Olivier Chapelle, Zaïd Harchaoui
Two algorithms to quickly and accurately estimate consumer preferences are proposed to bring tools from the machine learning community to solve the choice-based conjoint analysis problem more efficiently.
Linear Algebra and its ApplicationsFunctions that preserve families of positive semidefinite matrices
44 Citations1995Carl H. Fitzgerald, Charles A. Micchelli +1 more
The Annals of StatisticsNonlinear canonical analysis and independence tests
43 Citations1998Jacques Dauxois, Guy Martial Nkiet
International Conference on Machine LearningA Column Generation Algorithm For Boosting
39 Citations2000K. Bennet, Ayhan Demiriz +1 more
It is proved that minimizing the soft margin error function (equivalent to solving an LP) directly optimizes a generalization error bound and is used to solve any boosting LP by iteratively optimizing the dual classification costs in a restricted LP and dynamically generating weak learners to make new LP columns.
CORE Scholar (Wright State University)Gene Prediction with Conditional Random Fields
37 Citations2005Aron Culotta, David Kulp +1 more
This work proposes the use of a discriminitively trained sequence model, the conditional random field (CRF), which can naturally incorporate arbitrary, non-independent features of the input without making conditional independence assumptions among the features.
Uncertainty in Artificial IntelligenceExponential families for conditional random fields
32 Citations2004Yasemin Altün, Alex Smola +1 more
Lecture notes in computer scienceLearning grammatical structure using statistical decision-trees
17 Citations1996David M. Magerman
SPATTER, a statistical parser based on decision-tree learning techniques which avoids the difficulties of grammar development simply by having no grammar, is described.
Lecture notes in computer scienceLoss Bounds for Online Category Ranking
12 Citations2005Koby Crammer, Yoram Singer
This paper describes and analyzes several algorithms for online category ranking where the instances are revealed in a sequential manner and describes additive and multiplicative updates which constitute the core of the learning algorithms.
…