NDLI: Proceedings of the 23rd international conference on Machine learning (ICML '06)

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Using inaccurate models in reinforcement learning

Algorithms for portfolio management based on the Newton method

Higher order learning with graphs

Ranking on graph data

Robust probabilistic projections

A DC-programming algorithm for kernel selection

Relational temporal difference learning

A new approach to data driven clustering

Agnostic active learning

On a theory of learning with similarity functions

On Bayesian bounds

Convex optimization techniques for fitting sparse Gaussian graphical models

Cover trees for nearest neighbor

Graph model selection using maximum likelihood

Dynamic topic models

Predictive search distributions

Learning predictive state representations using non-blind policies

Efficient co-regularised least squares regression

Semi-supervised learning for structured output variables

Fast nonparametric clustering with Gaussian blurring mean-shift

An empirical comparison of supervised learning algorithms

Robust Euclidean embedding

Hierarchical classification: combining Bayes with SVM

A continuation method for semi-supervised SVMs

A regularization framework for multiple-instance learning

Trading convexity for scalability

Learning algorithms for online principal-agent problems (and selling goods online)

Dealing with non-stationary environments using context detection

Locally adaptive classification piloted by uncertainty

The relationship between Precision-Recall and ROC curves

Discriminative cluster analysis

Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations

Learning the structure of Factored Markov Decision Processes in reinforcement learning problems

Efficient learning of Naive Bayes classifiers under class-conditional classification noise

Learning user preferences for sets of objects

$R_{1}-PCA:$ rotational invariant $L_{1}-norm$ principal component analysis for robust subspace factorization

Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution

A graphical model for predicting protein molecular function

Qualitative reinforcement learning

Online multiclass learning by interclass hypothesis sharing

Regression with the optimised combination technique

A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian Inference

The rate adapting poisson model for information retrieval and object recognition

Kernelizing the output of tree-based methods

Nightmare at test time: robust learning by feature deletion

A choice model with infinitely many latent features

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Practical solutions to the problem of diagonal dominance in kernel document clustering

Fast transpose methods for kernel learning on sparse data

An analysis of graph cut size for transductive learning

Learning a kernel function for classification with small training samples

Looping suffix tree-based inference of partially observable hidden state

Batch mode active learning and its application to medical image classification

Ranking individuals by group comparisons

Hidden process models

Estimating relatedness via data compression

Automatic basis function construction for approximate dynamic programming and reinforcement learning

Personalized handwriting recognition via biased regularization

Optimal kernel selection in Kernel Fisher discriminant analysis

Pareto optimal linear classification

Fast particle smoothing: if I had a million particles

Autonomous shaping: knowledge transfer in reinforcement learning

Data association for topic intensity tracking

Learning low-rank kernel matrices

Local distance preservation in the GP-LVM through back constraints

Simpler knowledge-based support vector machines

Using query-specific variance estimates to combine Bayesian classifiers

A probabilistic model for text kernels

Efficient MAP approximation for dense energy functions

Nonstationary kernel combination

Region-based value iteration for partially observable Markov decision processes

Multiclass boosting with repartitioning

Pachinko allocation: DAG-structured mixture models of topic correlations

Spectral clustering for multi-type relational data

Combined central and subspace clustering for computer vision applications

Fast direct policy evaluation using multiscale analysis of Markov diffusion processes

Pruning in ordered bagging ensembles

Learning high-order MRF priors of color images

The uniqueness of a good optimum for K-means

Kernel information embeddings

Generalized spectral bounds for sparse LDA

Learning to impersonate

Online decoding of Markov models under latency constraints

Learning hierarchical task networks by observation

Reinforcement learning for optimized trade execution

Concept boundary detection for speeding up SVMs

The support vector decomposition machine

An analytic solution to discrete Bayesian reinforcement learning

MISSL: multiple-instance semi-supervised learning

Constructing informative priors using transfer learning

Maximum margin planning

Quadratic programming relaxations for metric labeling and Markov random field MAP estimation

Categorization in multiple category systems

How boosting the margin can also boost classifier complexity

Combining discriminative features to infer complex trajectories

Sequential update of ADtrees

Predictive linear-Gaussian models of controlled stochastic dynamical systems

A statistical approach to rule learning

Efficient inference on sequence segmentation models

Cost-sensitive learning with conditional Markov networks

Feature value acquisition in testing: a sequential batch test algorithm

Permutation invariant SVMs

Bayesian learning of measurement and structural models

An intrinsic reward mechanism for efficient exploration

Deterministic annealing for semi-supervised kernel machines

Feature subset selection bias for classification learning

Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features

An investigation of computational and informational limits in Gaussian mixture clustering

Bayesian pattern ranking for move prediction in the game of Go

PAC model-free reinforcement learning

Experience-efficient learning in associative bandit problems

Full Bayesian network classifiers

Local Fisher discriminant analysis for supervised dimensionality reduction

Iterative RELIEF for feature weighting

Multiclass reduced-set support vector machines

Fast and space efficient string kernels using suffix arrays

Bayesian regression with input noise for high dimensional data

Probabilistic inference for solving discrete and continuous state Markov Decision Processes

Clustering graphs by weighted substructure mining

Active sampling for detecting irrelevant features

Accelerated training of conditional random fields with stochastic gradient methods

Topic modeling: beyond bag-of-words

Label propagation through linear neighborhoods

Two-dimensional solution path for support vector regression

Totally corrective boosting algorithms that maximize the margin

Inference with the Universum

Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems

Predictive state representations with options

Fast time series classification using numerosity reduction

A duality view of spectral methods for dimensionality reduction

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture

Discriminative unsupervised learning of structured predictors

Semi-supervised nonlinear dimensionality reduction

Null space versus orthogonal linear discriminant analysis

Active learning via transductive experimental design

Collaborative ordinal regression

Block-quantized kernel matrix for fast spectral embedding

Statistical debugging: simultaneous identification of multiple bugs

Efficient lazy elimination for averaged one-dependence estimators

Proceedings of the 23rd international conference on Machine learning (ICML '06)

Content Provider	ACM Digital Library
Editor	Moore, Andrew Cohen, William
Copyright Year	2006
Abstract	This volume, which is also available from http://www.machinelearning.org, the home page of the International Machine Learning Society, contains the technical papers accepted for presentation at ICML-2006, the 23rd International Conference on Machine Learning. ICML is an international forum for presentation and discussion of the latest results in the field of machine learning. This year, ICML was held at Carnegie Mellon University, in Pittsburgh, Pennsylvania, and was co-located with COLT-2006, the 19th Annual Conference on Computational Learning Theory.Coincidentally, Carnegie Mellon University was also the venue for the first ICML---the First Machine Learning Workshop, which was held in 1980. Instead of proceedings, a book was published (Machine Learning: an Artificial Intelligence Approach, ed. Michalski, Carbonell, and Mitchell, Morgan Kaufman, 1983) containing sixteen research papers, and also a "comprehensive bibliography" of the field of machine learning, as it stood in 1983. This bibliography contained 572 entries.In 2006, no less than 548 papers were submitted to ICML---nearly as many as were in the "comprehensive bibliography" published with the papers from the first ICML. These papers were subjected to a thorough review process. In the first round of reviewing, every paper received three reviews by program committee members. Authors were then given an opportunity to view the first-round reviews and respond to them. Led by a Senior Program Committee member, the reviewers then engaged in a discussion of the paper, leading finally to a decision by the Senior Program Committee member in charge of the paper. Papers could be accepted, rejected, or conditionally accepted; the 36 conditionally accepted papers were subject to an additional final round of review by the Senior Program Committee. Of the 548 submissions, 140 were accepted for publication, an acceptance rate of 25.5%.In addition to the technical talks, ICML-2006 also included seven tutorials and eleven workshops, which were held before and after the conference, respectively. Authors presented their papers both orally and in a poster session, allowing time for detailed discussions with any interested attendees of the conference. Each day of the main conference included an invited talk by a prominent researcher. We were very fortunate to be able to host David Haussler, of the University of California at Santa Cruz; Robert Schapire, of Princeton University; and Mandyam V. Srinivasan, of the Australian National University.
Related Links	http://www.icml2006.org/icml2006/home.html
ISBN	1595933832
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-06-25
Access Restriction	Subscribed
Content Type	Text
Resource Type	Conference Proceedings

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