BibSLEIGH — Proceedings of the 21st International Conference on Machine Learning

Carla E. Brodley
Proceedings of the 21st International Conference on Machine Learning
ICML, 2004.

KER

@proceedings{ICML-2004,
	address       = "Banff, Alberta, Canada",
	editor        = "Carla E. Brodley",
	publisher     = "{ACM}",
	series        = "{ACM International Conference Proceeding Series}",
	title         = "{Proceedings of the 21st International Conference on Machine Learning}",
	volume        = 69,
	year          = 2004,
}

Contents (117 items)

ICML-2004-AgarwalT #3d #learning: Learning to track 3D human motion from silhouettes (AA, BT).
ICML-2004-AhnCO #algorithm #multi: A multiplicative up-propagation algorithm (JHA, SC, JHO).
ICML-2004-AltunHS #classification #process #sequence: Gaussian process classification for segmenting and annotating sequences (YA, TH, AJS).
ICML-2004-AppiceCRF #multi #problem: Redundant feature elimination for multi-class problems (AA, MC, SR, PAF).
ICML-2004-BachLJ #algorithm #kernel #learning #multi: Multiple kernel learning, conic duality, and the SMO algorithm (FRB, GRGL, MIJ).
ICML-2004-BahamondeBDQLCAG #case study #learning #set: Feature subset selection for learning preferences: a case study (AB, GFB, JD, JRQ, OL, JJdC, JA, FG).
ICML-2004-BanerjeeDGM #analysis #estimation #exponential #product line: An information theoretic analysis of maximum likelihood mixture estimation for exponential families (AB, ISD, JG, SM).
ICML-2004-BasilicoH #collaboration: Unifying collaborative and content-based filtering (JB, TH).
ICML-2004-BaskiotisS #approach: C4.5 competence map: a phase transition-inspired approach (NB, MS).
ICML-2004-BilenkoBM #clustering #constraints #learning #metric: Integrating constraints and metric learning in semi-supervised clustering (MB, SB, RJM).
ICML-2004-BleiJ #process: Variational methods for the Dirichlet process (DMB, MIJ).
ICML-2004-BlumLRR #learning #random #using: Semi-supervised learning using randomized mincuts (AB, JDL, MRR, RR).
ICML-2004-BohteBG #classification #parametricity #polynomial: Nonparametric classification with polynomial MPMC cascades (SMB, MB, GZG).
ICML-2004-Bouckaert #classification #learning: Estimating replicability of classifier learning experiments (RRB).
ICML-2004-BrefeldS #learning: Co-EM support vector learning (UB, TS).
ICML-2004-Brinker #learning #ranking: Active learning of label ranking functions (KB).
ICML-2004-CaruanaNCK #library #modelling: Ensemble selection from libraries of models (RC, ANM, GC, AK).
ICML-2004-CastilloW #case study #comparative #learning #multi: A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning (LPC, SW).
ICML-2004-ChangY #adaptation #clustering #linear #metric: Locally linear metric adaptation for semi-supervised clustering (HC, DYY).
ICML-2004-ChuGW #predict #visual notation: A graphical model for protein secondary structure prediction (WC, ZG, DLW).
ICML-2004-ClimerZ #approach #clustering: Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering (SC, WZ).
ICML-2004-CollobertB: Links between perceptrons, MLPs and SVMs (RC, SB).
ICML-2004-ConitzerS #bound #communication #complexity #game studies #learning: Communication complexity as a lower bound for learning in games (VC, TS).
ICML-2004-CortesM #kernel: Distribution kernels based on moments of counts (CC, MM).
ICML-2004-CrammerC #optimisation: A needle in a haystack: local one-class optimization (KC, GC).
ICML-2004-DekelKS #classification #scalability: Large margin hierarchical classification (OD, JK, YS).
ICML-2004-DietterichAB #random: Training conditional random fields via gradient tree boosting (TGD, AA, YB).
ICML-2004-DingH #clustering: Linearized cluster assignment via spectral ordering (CHQD, XH).
ICML-2004-DingH04a #analysis #clustering #component: K-means clustering via principal component analysis (CHQD, XH).
ICML-2004-DSouzaVS: The Bayesian backfitting relevance vector machine (AD, SV, SS).
ICML-2004-EliazarP #learning #mobile #modelling #probability: Learning probabilistic motion models for mobile robots (AIE, RP).
ICML-2004-EsmeirM #algorithm #induction: Lookahead-based algorithms for anytime induction of decision trees (SE, SM).
ICML-2004-EspositoS #analysis #classification #monte carlo: A Monte Carlo analysis of ensemble classification (RE, LS).
ICML-2004-FernB #clustering #graph #problem: Solving cluster ensemble problems by bipartite graph partitioning (XZF, CEB).
ICML-2004-FernG #relational #reliability: Relational sequential inference with reliable observations (AF, RG).
ICML-2004-FerriFH #classification: Delegating classifiers (CF, PAF, JHO).
ICML-2004-Forman #classification #feature model #multi: A pitfall and solution in multi-class feature selection for text classification (GF).
ICML-2004-FrankK #multi #problem: Ensembles of nested dichotomies for multi-class problems (EF, SK).
ICML-2004-FungDBR #algorithm #kernel #performance #using: A fast iterative algorithm for fisher discriminant using heterogeneous kernels (GF, MD, JB, RBR).
ICML-2004-GabrilovichM #categorisation #feature model #using: Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5 (EG, SM).
ICML-2004-GaoWLC #approach #categorisation #learning #multi #robust: A MFoM learning approach to robust multiclass multi-label text categorization (SG, WW, CHL, TSC).
ICML-2004-Gilad-BachrachNT #algorithm #feature model: Margin based feature selection — theory and algorithms (RGB, AN, NT).
ICML-2004-GoldenbergM #learning #scalability: Tractable learning of large Bayes net structures from sparse data (AG, AWM).
ICML-2004-GramacyLM #parametricity #process: Parameter space exploration with Gaussian process trees (RBG, HKHL, WGM).
ICML-2004-GrossmanD #classification #learning #network: Learning Bayesian network classifiers by maximizing conditional likelihood (DG, PMD).
ICML-2004-HamLMS #kernel #reduction: A kernel view of the dimensionality reduction of manifolds (JH, DDL, SM, BS).
ICML-2004-HardinTA #feature model #linear: A theoretical characterization of linear SVM-based feature selection (DPH, IT, CFA).
ICML-2004-HerschtalR #optimisation #using: Optimising area under the ROC curve using gradient descent (AH, BR).
ICML-2004-HertzBW #clustering #distance: Boosting margin based distance functions for clustering (TH, ABH, DW).
ICML-2004-HuangYKL #classification #learning #scalability: Learning large margin classifiers locally and globally (KH, HY, IK, MRL).
ICML-2004-JakulinB #interactive #testing: Testing the significance of attribute interactions (AJ, IB).
ICML-2004-JamesS #learning #predict: Learning and discovery of predictive state representations in dynamical systems with reset (MRJ, SPS).
ICML-2004-JanodetNSS #grammar inference: Boosting grammatical inference with confidence oracles (JCJ, RN, MS, HMS).
ICML-2004-Jebara #kernel #multi: Multi-task feature and kernel selection for SVMs (TJ).
ICML-2004-JenkinsM #reduction: A spatio-temporal extension to Isomap nonlinear dimension reduction (OCJ, MJM).
ICML-2004-JinL #induction #robust: Robust feature induction for support vector machines (RJ, HL).
ICML-2004-KashimaT #algorithm #graph #kernel #learning #sequence: Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs (HK, YT).
ICML-2004-KerstingOR #relational: Bellman goes relational (KK, MvO, LDR).
ICML-2004-KimK #feature model: Gradient LASSO for feature selection (YK, JK).
ICML-2004-KokV: Sparse cooperative Q-learning (JRK, NAV).
ICML-2004-KoppelS #classification #problem #verification: Authorship verification as a one-class classification problem (MK, JS).
ICML-2004-KrauseS: Leveraging the margin more carefully (NK, YS).
ICML-2004-LaffertyZL #clique #kernel #random #representation: Kernel conditional random fields: representation and clique selection (JDL, XZ, YL).
ICML-2004-LawrenceP #learning: Learning to learn with the informative vector machine (NDL, JCP).
ICML-2004-LebanonL #classification #multi: Hyperplane margin classifiers on the multinomial manifold (GL, JDL).
ICML-2004-LeeWZB #perspective #probability: Probabilistic tangent subspace: a unified view (JL, JW, CZ, ZB).
ICML-2004-LiMO #category theory #clustering: Entropy-based criterion in categorical clustering (TL, SM, MO).
ICML-2004-LingYWZ #low cost: Decision trees with minimal costs (CXL, QY, JW, SZ).
ICML-2004-MaheUAPV #graph #kernel: Extensions of marginalized graph kernels (PM, NU, TA, JLP, JPV).
ICML-2004-MannorMHK #abstraction #clustering #learning: Dynamic abstraction in reinforcement learning via clustering (SM, IM, AH, UK).
ICML-2004-MannorSST #bias #estimation: Bias and variance in value function estimation (SM, DS, PS, JNT).
ICML-2004-MarlinZ #collaboration #multi: The multiple multiplicative factor model for collaborative filtering (BMM, RSZ).
ICML-2004-MelvilleM #learning: Diverse ensembles for active learning (PM, RJM).
ICML-2004-MerkeS #approximate #convergence #learning #linear: Convergence of synchronous reinforcement learning with linear function approximation (AM, RS).
ICML-2004-MoralesS #behaviour #learning: Learning to fly by combining reinforcement learning with behavioural cloning (EFM, CS).
ICML-2004-NatteeSNO #first-order #learning #mining #multi: Learning first-order rules from data with multiple parts: applications on mining chemical compound data (CN, SS, MN, TO).
ICML-2004-NguyenS #clustering #learning #using: Active learning using pre-clustering (HTN, AWMS).
ICML-2004-NguyenWJ #classification #detection #distributed #kernel #using: Decentralized detection and classification using kernel methods (XN, MJW, MIJ).
ICML-2004-OngMCS #kernel #learning: Learning with non-positive kernels (CSO, XM, SC, AJS).
ICML-2004-PeltonenSK #finite: Sequential information bottleneck for finite data (JP, JS, SK).
ICML-2004-PhillipsDS #approach #modelling: A maximum entropy approach to species distribution modeling (SJP, MD, RES).
ICML-2004-PieterN #learning: Apprenticeship learning via inverse reinforcement learning (PA, AYN).
ICML-2004-Potts #incremental #learning #linear: Incremental learning of linear model trees (DP).
ICML-2004-QiMPG #automation #predict: Predictive automatic relevance determination by expectation propagation (Y(Q, TPM, RWP, ZG).
ICML-2004-RayP #algorithm: Sequential skewing: an improved skewing algorithm (SR, DP).
ICML-2004-RosalesAF #clustering #learning #using: Learning to cluster using local neighborhood structure (RR, KA, BJF).
ICML-2004-RosencrantzGT #learning #predict: Learning low dimensional predictive representations (MR, GJG, ST).
ICML-2004-Rosset: Model selection via the AUC (SR).
ICML-2004-RuckertK #bound #learning #towards: Towards tight bounds for rule learning (UR, SK).
ICML-2004-RudarySP #adaptation #constraints #learning #reasoning: Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning (MRR, SPS, MEP).
ICML-2004-Ryabko #learning #online: Online learning of conditionally I.I.D. data (DR).
ICML-2004-ScullyML: Coalition calculation in a dynamic agent environment (TS, MGM, GL).
ICML-2004-Shalev-ShwartzSN #learning #online #pseudo: Online and batch learning of pseudo-metrics (SSS, YS, AYN).
ICML-2004-SimsekB #abstraction #identification #learning #using: Using relative novelty to identify useful temporal abstractions in reinforcement learning (ÖS, AGB).
ICML-2004-SminchisescuJ #embedded #generative #modelling #visual notation: Generative modeling for continuous non-linearly embedded visual inference (CS, ADJ).
ICML-2004-Strens #performance #policy: Efficient hierarchical MCMC for policy search (MJAS).
ICML-2004-SuD #automation #component #probability: Automated hierarchical mixtures of probabilistic principal component analyzers (TS, JGD).
ICML-2004-SuttonRM #modelling #probability #random #sequence: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data (CAS, KR, AM).
ICML-2004-SzepesvariS: Interpolation-based Q-learning (CS, WDS).
ICML-2004-TaoSVO #approximate #learning #multi: SVM-based generalized multiple-instance learning via approximate box counting (QT, SDS, NVV, TTO).
ICML-2004-TaskarCK #learning #markov #network: Learning associative Markov networks (BT, VC, DK).
ICML-2004-ToutanovaMN #dependence #learning #modelling #random #word: Learning random walk models for inducing word dependency distributions (KT, CDM, AYN).
ICML-2004-TsochantaridisHJA #machine learning: Support vector machine learning for interdependent and structured output spaces (IT, TH, TJ, YA).
ICML-2004-VuralD #multi: A hierarchical method for multi-class support vector machines (VV, JGD).
ICML-2004-WeinbergerSS #kernel #learning #matrix #reduction: Learning a kernel matrix for nonlinear dimensionality reduction (KQW, FS, LKS).
ICML-2004-WellingRT #approximate #markov: Approximate inference by Markov chains on union spaces (MW, MRZ, YWT).
ICML-2004-WierstraW #markov #modelling: Utile distinction hidden Markov models (DW, MW).
ICML-2004-WingateS #named #parallel: P3VI: a partitioned, prioritized, parallel value iterator (DW, KDS).
ICML-2004-WuD #data flow: Improving SVM accuracy by training on auxiliary data sources (PW, TGD).
ICML-2004-XingSJ #process #type inference: Bayesian haplo-type inference via the dirichlet process (EPX, RS, MIJ).
ICML-2004-Ye #approximate #matrix #rank: Generalized low rank approximations of matrices (JY).
ICML-2004-YeJLP #analysis #feature model #linear: Feature extraction via generalized uncorrelated linear discriminant analysis (JY, RJ, QL, HP).
ICML-2004-Zadrozny #bias #classification #learning: Learning and evaluating classifiers under sample selection bias (BZ).
ICML-2004-Zhang #algorithm #linear #predict #probability #problem #scalability #using: Solving large scale linear prediction problems using stochastic gradient descent algorithms (TZ0).
ICML-2004-ZhangKY #algorithm: Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model (ZZ, JTK, DYY).
ICML-2004-ZhangY #estimation #probability: Probabilistic score estimation with piecewise logistic regression (JZ, YY).
ICML-2004-ZhangYK #algorithm #kernel #learning #matrix #using: Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm (ZZ, DYY, JTK).

Carla E. BrodleyProceedings of the 21st International Conference on Machine LearningICML, 2004.

Contents (117 items)

Carla E. Brodley
Proceedings of the 21st International Conference on Machine Learning
ICML, 2004.