icml 2007 论文列表Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007. |
Multiclass multiple kernel learning.
Transductive support vector machines for structured variables.
Dynamic hierarchical Markov random fields and their application to web data extraction.
On the relation between multi-instance learning and semi-supervised learning.
Spectral clustering and transductive learning with multiple views.
Spectral feature selection for supervised and unsupervised learning.
Conditional random fields for multi-agent reinforcement learning.
Optimal dimensionality of metric space for classification.
Nonlinear independent component analysis with minimal nonlinear distortion.
Maximum margin clustering made practical.
On the value of pairwise constraints in classification and consistency.
Robust multi-task learning with t-processes.
Discriminant kernel and regularization parameter learning via semidefinite programming.
Least squares linear discriminant analysis.
Asymptotic Bayesian generalization error when training and test distributions are different.
Map building without localization by dimensionality reduction techniques.
The matrix stick-breaking process for flexible multi-task learning.
Modeling changing dependency structure in multivariate time series.
On learning linear ranking functions for beam search.
Local learning projections.
Learning to combine distances for complex representations.
Beamforming using the relevance vector machine.
Multi-task reinforcement learning: a hierarchical Bayesian approach.
What is decreased by the max-sum arc consistency algorithm?
Winnowing subspaces.
On learning with dissimilarity functions.
Hybrid huberized support vector machines for microarray classification.
Multifactor Gaussian process models for style-content separation.
Transductive regression piloted by inter-manifold relations.
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data.
A kernel path algorithm for support vector machines.
Learning from interpretations: a rooted kernel for ordered hypergraphs.
Experimental perspectives on learning from imbalanced data.
Discriminative Gaussian process latent variable model for classification.
Entire regularization paths for graph data.
Simpler core vector machines with enclosing balls.
Approximate maximum margin algorithms with rules controlled by the number of mistakes.
Classifying matrices with a spectral regularization.
Incremental Bayesian networks for structure prediction.
Cross-domain transfer for reinforcement learning.
On the role of tracking in stationary environments.
Piecewise pseudolikelihood for efficient training of conditional random fields.
A kernel-based causal learning algorithm.
Robust mixtures in the presence of measurement errors.
Learning to solve game trees.
Sparse eigen methods by D.C. programming.
Supervised feature selection via dependence estimation.
A dependence maximization view of clustering.
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM.
Sample compression bounds for decision trees.
Restricted Boltzmann machines for collaborative filtering.
Graph clustering with network structure indices.
More efficiency in multiple kernel learning.
Online discovery of similarity mappings.
Self-taught learning: transfer learning from unlabeled data.
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation.
Reinforcement learning by reward-weighted regression for operational space control.
Analyzing feature generation for value-function approximation.
Learning for efficient retrieval of structured data with noisy queries.
Multi-armed bandit problems with dependent arms.
A fast linear separability test by projection of positive points on subspaces.
Learning state-action basis functions for hierarchical MDPs.
Regression on manifolds using kernel dimension reduction.
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process.
Comparisons of sequence labeling algorithms and extensions.
Revisiting probabilistic models for clustering with pair-wise constraints.
Unsupervised estimation for noisy-channel models.
Dimensionality reduction and generalization.
Fast and effective kernels for relational learning from texts.
Three new graphical models for statistical language modelling.
Mixtures of hierarchical topics with Pachinko allocation.
Bottom-up learning of Markov logic network structure.
Linear and nonlinear generative probabilistic class models for shape contours.
Asymmetric boosting.
Automatic shaping and decomposition of reward functions.
Simple, robust, scalable semi-supervised learning via expectation regularization.
Adaptive mesh compression in 3D computer graphics using multiscale manifold learning.
Discriminant analysis in correlation similarity measure space.
Relational clustering by symmetric convex coding.
Trust region Newton methods for large-scale logistic regression.
Quadratically gated mixture of experts for incomplete data classification.
A permutation-augmented sampler for DP mixture models.
A novel orthogonal NMF-based belief compression for POMDPs.
Large-scale RLSC learning without agony.
Adaptive dimension reduction using discriminant analysis and K-means clustering.
A transductive framework of distance metric learning by spectral dimensionality reduction.
Support cluster machine.
Scalable modeling of real graphs using Kronecker multiplication.
Learning a meta-level prior for feature relevance from multiple related tasks.
Hierarchical Gaussian process latent variable models.
An empirical evaluation of deep architectures on problems with many factors of variation.
Online kernel PCA with entropic matrix updates.
On one method of non-diagonal regularization in sparse Bayesian learning.
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach.
Kernelizing PLS, degrees of freedom, and efficient model selection.
Statistical predicate invention.
Local dependent components.
Infinite mixtures of trees.
A recursive method for discriminative mixture learning.
Neighbor search with global geometry: a minimax message passing algorithm.
Most likely heteroscedastic Gaussian process regression.
Constructing basis functions from directed graphs for value function approximation.
Bayesian compressive sensing and projection optimization.
Parameter learning for relational Bayesian networks.
Learning nonparametric kernel matrices from pairwise constraints.
A bound on the label complexity of agnostic active learning.
Supervised clustering of streaming data for email batch detection.
Sparse probabilistic classifiers.
Efficient inference with cardinality-based clique potentials.
Recovering temporally rewiring networks: a model-based approach.
Best of both: a hybridized centroid-medoid clustering heuristic.
Exponentiated gradient algorithms for log-linear structured prediction.
Bayesian actor-critic algorithms.
Gradient boosting for kernelized output spaces.
Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps.
Combining online and offline knowledge in UCT.
Manifold-adaptive dimension estimation.
CarpeDiem: an algorithm for the fast evaluation of SSL classifiers.
Hierarchical maximum entropy density estimation.
Non-isometric manifold learning: analysis and an algorithm.
Unsupervised prediction of citation influences.
Percentile optimization in uncertain Markov decision processes with application to efficient exploration.
An integrated approach to feature invention and model construction for drug activity prediction.
Information-theoretic metric learning.
Intractability and clustering with constraints.
Boosting for transfer learning.
Kernel selection forl semi-supervised kernel machines.
Full regularization path for sparse principal component analysis.
Magnitude-preserving ranking algorithms.
Learning to compress images and videos.
Minimum reference set based feature selection for small sample classifications.
Direct convex relaxations of sparse SVM.
Local similarity discriminant analysis.
Learning to rank: from pairwise approach to listwise approach.
Feature selection in a kernel space.
Cluster analysis of heterogeneous rank data.
Multiple instance learning for sparse positive bags.
Efficiently computing minimax expected-size confidence regions.
Solving multiclass support vector machines with LaRank.
Discriminative learning for differing training and test distributions.
Structural alignment based kernels for protein structure classification.
Learning distance function by coding similarity.
Focused crawling with scalable ordinal regression solvers.
Rashmin Babaria J. Saketha Nath S. Krishnan K. R. Sivaramakrishnan Chiranjib Bhattacharyya M. Narasimha Murty
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks.
Multiclass core vector machine.
Scalable training of L1-regularized log-linear models.
Two-view feature generation model for semi-supervised learning.
Uncovering shared structures in multiclass classification.
Learning random walks to rank nodes in graphs.
Quantum clustering algorithms.