Luc De Raedt, Stefan Wrobel
Proceedings of the 22nd International Conference on Machine Learning
ICML, 2005.

@proceedings{ICML-2005,
	address       = "Bonn, Germany",
	editor        = "Luc De Raedt and Stefan Wrobel",
	isbn          = "1-59593-180-5",
	publisher     = "{ACM}",
	series        = "{ACM International Conference Proceeding Series}",
	title         = "{Proceedings of the 22nd International Conference on Machine Learning}",
	volume        = 119,
	year          = 2005,
}

Contents (134 items)

45 ×#learning
21 ×#classification
18 ×#modelling
15 ×#using
11 ×#predict
10 ×#clustering
9 ×#multi
9 ×#performance
7 ×#analysis
7 ×#graph

ICML-2005-AbbeelN #learning

Exploration and apprenticeship learning in reinforcement learning (PA, AYN), pp. 1–8.

ICML-2005-AndersonM #algorithm #learning #markov #modelling

Active learning for Hidden Markov Models: objective functions and algorithms (BA, AM), pp. 9–16.

ICML-2005-AngelopoulosC

Tempering for Bayesian C&RT (NA, JC), pp. 17–24.

ICML-2005-Angiulli #nearest neighbour #performance

Fast condensed nearest neighbor rule (FA), pp. 25–32.

ICML-2005-BachJ #composition #kernel #predict #rank

Predictive low-rank decomposition for kernel methods (FRB, MIJ), pp. 33–40.

ICML-2005-BekkermanEM #clustering #interactive #multi

Multi-way distributional clustering via pairwise interactions (RB, REY, AM), pp. 41–48.

ICML-2005-BeygelzimerDHLZ #classification #fault #reduction

Error limiting reductions between classification tasks (AB, VD, TPH, JL, BZ), pp. 49–56.

ICML-2005-BlockeelPS #learning #multi

Multi-instance tree learning (HB, DP, AS), pp. 57–64.

ICML-2005-BowlingGW

Action respecting embedding (MHB, AG, DFW), pp. 65–72.

ICML-2005-BreitenbachG #clustering #ranking

Clustering through ranking on manifolds (MB, GZG), pp. 73–80.

ICML-2005-BridewellALT #induction #process

Reducing overfitting in process model induction (WB, NBA, PL, LT), pp. 81–88.

ICML-2005-BurgesSRLDHH #learning #rank #using

Learning to rank using gradient descent (CJCB, TS, ER, AL, MD, NH, GNH), pp. 89–96.

ICML-2005-BurgeL #learning #network

Learning class-discriminative dynamic Bayesian networks (JB, TL), pp. 97–104.

ICML-2005-CalinonB #framework #gesture #probability #recognition #using

Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM (SC, AB), pp. 105–112.

ICML-2005-CarneyCDL #network #predict #probability #using

Predicting probability distributions for surf height using an ensemble of mixture density networks (MC, PC, JD, CL), pp. 113–120.

ICML-2005-ChangK #learning

Hedged learning: regret-minimization with learning experts (YHC, LPK), pp. 121–128.

ICML-2005-ChengJSW #image #modelling

Variational Bayesian image modelling (LC, FJ, DS, SW), pp. 129–136.

ICML-2005-ChuG #learning #process

Preference learning with Gaussian processes (WC, ZG), pp. 137–144.

ICML-2005-ChuK

New approaches to support vector ordinal regression (WC, SSK), pp. 145–152.

ICML-2005-CortesMW #learning

A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.

ICML-2005-CrandallG #game studies #learning

Learning to compete, compromise, and cooperate in repeated general-sum games (JWC, MAG), pp. 161–168.

ICML-2005-DaumeM #approximate #learning #optimisation #predict #scalability

Learning as search optimization: approximate large margin methods for structured prediction (HDI, DM), pp. 169–176.

ICML-2005-TorreK #analysis #multimodal

Multimodal oriented discriminant analysis (FDlT, TK), pp. 177–184.

ICML-2005-DrakeV #learning

A practical generalization of Fourier-based learning (AD, DV), pp. 185–192.

ICML-2005-DriessensD #first-order #learning #modelling

Combining model-based and instance-based learning for first order regression (KD, SD), pp. 193–200.

ICML-2005-EngelMM #learning #process

Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.

ICML-2005-EspositoS #classification #comparison #monte carlo

Experimental comparison between bagging and Monte Carlo ensemble classification (RE, LS), pp. 209–216.

ICML-2005-FinleyJ #clustering

Supervised clustering with support vector machines (TF, TJ), pp. 217–224.

ICML-2005-FrohlichWSZ #graph #kernel

Optimal assignment kernels for attributed molecular graphs (HF, JKW, FS, AZ), pp. 225–232.

ICML-2005-GeurtsW #modelling

Closed-form dual perturb and combine for tree-based models (PG, LW), pp. 233–240.

ICML-2005-GirolamiR #kernel #learning #modelling

Hierarchic Bayesian models for kernel learning (MG, SR), pp. 241–248.

ICML-2005-GlocerET #classification #feature model #online

Online feature selection for pixel classification (KAG, DE, JT), pp. 249–256.

ICML-2005-GroisW #approach #comprehension #learning

Learning strategies for story comprehension: a reinforcement learning approach (EG, DCW), pp. 257–264.

ICML-2005-GuestrinKS #process

Near-optimal sensor placements in Gaussian processes (CG, AK, APS), pp. 265–272.

ICML-2005-GuptaG #clustering #hybrid #robust #using

Robust one-class clustering using hybrid global and local search (GG, JG), pp. 273–280.

ICML-2005-HeCM #analysis #locality #statistics

Statistical and computational analysis of locality preserving projection (XH, DC, WM), pp. 281–288.

ICML-2005-HeinA #estimation

Intrinsic dimensionality estimation of submanifolds in Rd (MH, JYA), pp. 289–296.

ICML-2005-HellerG #clustering

Bayesian hierarchical clustering (KAH, ZG), pp. 297–304.

ICML-2005-HerbsterPW #graph #learning #online

Online learning over graphs (MH, MP, LW), pp. 305–312.

ICML-2005-HillD #adaptation #classification #problem

Adapting two-class support vector classification methods to many class problems (SIH, AD), pp. 313–320.

ICML-2005-Ho #concept #data type #detection #framework

A martingale framework for concept change detection in time-varying data streams (SSH), pp. 321–327.

ICML-2005-IeWNL #adaptation #multi #recognition #using

Multi-class protein fold recognition using adaptive codes (EI, JW, WSN, CSL), pp. 329–336.

ICML-2005-IlghamiMNA #approximate #learning

Learning approximate preconditions for methods in hierarchical plans (OI, HMA, DSN, DWA), pp. 337–344.

ICML-2005-IresonCCFKL #information management #machine learning

Evaluating machine learning for information extraction (NI, FC, MEC, DF, NK, AL), pp. 345–352.

ICML-2005-JinCS #information retrieval #using

Learn to weight terms in information retrieval using category information (RJ, JYC, LS), pp. 353–360.

ICML-2005-JinZ #algorithm #probability #using

A smoothed boosting algorithm using probabilistic output codes (RJ, JZ), pp. 361–368.

ICML-2005-JingPR #classification #learning #naive bayes #network #performance

Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes (YJ, VP, JMR), pp. 369–376.

ICML-2005-Joachims #metric #multi #performance

A support vector method for multivariate performance measures (TJ), pp. 377–384.

ICML-2005-JoachimsH #bound #clustering #correlation #fault

Error bounds for correlation clustering (TJ, JEH), pp. 385–392.

ICML-2005-JodogneP #interactive #learning #visual notation

Interactive learning of mappings from visual percepts to actions (SJ, JHP), pp. 393–400.

ICML-2005-JonssonB #approach #composition

A causal approach to hierarchical decomposition of factored MDPs (AJ, AGB), pp. 401–408.

ICML-2005-KaariainenL #bound #comparison #fault

A comparison of tight generalization error bounds (MK, JL), pp. 409–416.

ICML-2005-Keerthi #classification #effectiveness #feature model

Generalized LARS as an effective feature selection tool for text classification with SVMs (SSK), pp. 417–424.

ICML-2005-KhoussainovHK #classification

Ensembles of biased classifiers (RK, AH, NK), pp. 425–432.

ICML-2005-KoivistoS #aspect-oriented #modelling

Computational aspects of Bayesian partition models (MK, KS), pp. 433–440.

ICML-2005-KokD #learning #logic #markov #network

Learning the structure of Markov logic networks (SK, PMD), pp. 441–448.

ICML-2005-KolterM #concept #using

Using additive expert ensembles to cope with concept drift (JZK, MAM), pp. 449–456.

ICML-2005-KulisBDM #approach #clustering #graph #kernel

Semi-supervised graph clustering: a kernel approach (BK, SB, ISD, RJM), pp. 457–464.

ICML-2005-LalSHP #feedback #interface #online

A brain computer interface with online feedback based on magnetoencephalography (TNL, MS, NJH, HP, TH, JM, MB, WR, TH, NB, BS), pp. 465–472.

ICML-2005-LangfordZ #classification #learning #performance

Relating reinforcement learning performance to classification performance (JL, BZ), pp. 473–480.

ICML-2005-LavioletteM #bound #classification

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers (FL, MM), pp. 481–488.

ICML-2005-LeSC #process

Heteroscedastic Gaussian process regression (QVL, AJS, SC), pp. 489–496.

ICML-2005-LeiteB #classification #performance #predict

Predicting relative performance of classifiers from samples (RL, PB), pp. 497–503.

ICML-2005-LiaoXC #data flow

Logistic regression with an auxiliary data source (XL, YX, LC), pp. 505–512.

ICML-2005-LiuXC #graph #predict #using

Predicting protein folds with structural repeats using a chain graph model (YL, EPX, JGC), pp. 513–520.

ICML-2005-LongVGTS #integration

Unsupervised evidence integration (PML, VV, SG, MT, RAS), pp. 521–528.

ICML-2005-LowdD #estimation #modelling #naive bayes #probability

Naive Bayes models for probability estimation (DL, PMD), pp. 529–536.

ICML-2005-MacskassyPR #empirical #evaluation

ROC confidence bands: an empirical evaluation (SAM, FJP, SR), pp. 537–544.

ICML-2005-MadsenKE #modelling #using #word

Modeling word burstiness using the Dirichlet distribution (REM, DK, CE), pp. 545–552.

ICML-2005-Mahadevan #learning

Proto-value functions: developmental reinforcement learning (SM), pp. 553–560.

ICML-2005-MannorPR #classification

The cross entropy method for classification (SM, DP, RYR), pp. 561–568.

ICML-2005-McMahanLG #bound #performance #programming #realtime

Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees (HBM, ML, GJG), pp. 569–576.

ICML-2005-Meila #axiom #clustering #perspective

Comparing clusterings: an axiomatic view (MM), pp. 577–584.

ICML-2005-MenchettiCF #composition #kernel

Weighted decomposition kernels (SM, FC, PF), pp. 585–592.

ICML-2005-MichelsSN #learning #using

High speed obstacle avoidance using monocular vision and reinforcement learning (JM, AS, AYN), pp. 593–600.

ICML-2005-NatarajanT #learning #multi

Dynamic preferences in multi-criteria reinforcement learning (SN, PT), pp. 601–608.

ICML-2005-NatarajanTADFR #first-order #learning #modelling #probability

Learning first-order probabilistic models with combining rules (SN, PT, EA, TGD, AF, ACR), pp. 609–616.

ICML-2005-NguyenH #performance

An efficient method for simplifying support vector machines (DN, TBH), pp. 617–624.

ICML-2005-Niculescu-MizilC #learning #predict

Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.

ICML-2005-OntanonP #learning #multi

Recycling data for multi-agent learning (SO, EP), pp. 633–640.

ICML-2005-PaiementEBB #embedded #visual notation

A graphical model for chord progressions embedded in a psychoacoustic space (JFP, DE, SB, DB), pp. 641–648.

ICML-2005-PalettaFS #recognition #visual notation

Q-learning of sequential attention for visual object recognition from informative local descriptors (LP, GF, CS), pp. 649–656.

ICML-2005-PernkopfB #classification #generative #learning #network #parametricity

Discriminative versus generative parameter and structure learning of Bayesian network classifiers (FP, JAB), pp. 657–664.

ICML-2005-Pietraszek #analysis #classification #optimisation #using

Optimizing abstaining classifiers using ROC analysis (TP), pp. 665–672.

ICML-2005-PoczosL #analysis #independence #using

Independent subspace analysis using geodesic spanning trees (BP, AL), pp. 673–680.

ICML-2005-RamakrishnanCKB #approximate #classification

A model for handling approximate, noisy or incomplete labeling in text classification (GR, KPC, RK, PB), pp. 681–688.

ICML-2005-RasmussenQ

Healing the relevance vector machine through augmentation (CER, JQC), pp. 689–696.

ICML-2005-RayC #comparison #empirical #learning #multi

Supervised versus multiple instance learning: an empirical comparison (SR, MC), pp. 697–704.

ICML-2005-RayP

Generalized skewing for functions with continuous and nominal attributes (SR, DP), pp. 705–712.

ICML-2005-RennieS #collaboration #matrix #performance #predict

Fast maximum margin matrix factorization for collaborative prediction (JDMR, NS), pp. 713–719.

ICML-2005-RohanimaneshM #approach #concurrent #generative #markov #named #process

Coarticulation: an approach for generating concurrent plans in Markov decision processes (KR, SM), pp. 720–727.

ICML-2005-RosellHRP #learning #why

Why skewing works: learning difficult Boolean functions with greedy tree learners (BR, LH, SR, DP), pp. 728–735.

ICML-2005-RothY #integer #linear #programming #random

Integer linear programming inference for conditional random fields (DR, WtY), pp. 736–743.

ICML-2005-RousuSSS #classification #learning #modelling #multi

Learning hierarchical multi-category text classification models (JR, CS, SS, JST), pp. 744–751.

ICML-2005-SalojarviPK #algorithm

Expectation maximization algorithms for conditional likelihoods (JS, KP, SK), pp. 752–759.

ICML-2005-SajamaO #distance #metric

Estimating and computing density based distance metrics (S, AO), pp. 760–767.

ICML-2005-SajamaO05a #modelling #reduction #using

Supervised dimensionality reduction using mixture models (S, AO), pp. 768–775.

ICML-2005-ScholkopfSB #machine learning #problem

Object correspondence as a machine learning problem (BS, FS, VB), pp. 776–783.

ICML-2005-ShaS #analysis #reduction

Analysis and extension of spectral methods for nonlinear dimensionality reduction (FS, LKS), pp. 784–791.

ICML-2005-ShashuaH #statistics

Non-negative tensor factorization with applications to statistics and computer vision (AS, TH), pp. 792–799.

ICML-2005-SiddiqiM #learning #performance

Fast inference and learning in large-state-space HMMs (SMS, AWM), pp. 800–807.

ICML-2005-SilvaS #identification #learning #modelling

New d-separation identification results for learning continuous latent variable models (RBdAeS, RS), pp. 808–815.

ICML-2005-SimsekWB #clustering #graph #identification #learning

Identifying useful subgoals in reinforcement learning by local graph partitioning (ÖS, APW, AGB), pp. 816–823.

ICML-2005-SindhwaniNB #learning

Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.

ICML-2005-SinghPGBB #analysis #learning

Active learning for sampling in time-series experiments with application to gene expression analysis (RS, NP, DKG, BB, ZBJ), pp. 832–839.

ICML-2005-SnelsonG #approximate #predict

Compact approximations to Bayesian predictive distributions (ES, ZG), pp. 840–847.

ICML-2005-SonnenburgRS #classification #scalability #sequence

Large scale genomic sequence SVM classifiers (SS, GR, BS), pp. 848–855.

ICML-2005-StrehlL #analysis #estimation #modelling

A theoretical analysis of Model-Based Interval Estimation (ALS, MLL), pp. 856–863.

ICML-2005-SunD #approach #learning

Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning (QS, GD), pp. 864–871.

ICML-2005-SunTLW #framework

Unifying the error-correcting and output-code AdaBoost within the margin framework (YS, ST, JL, DW), pp. 872–879.

ICML-2005-SzepesvariM #bound #finite

Finite time bounds for sampling based fitted value iteration (CS, RM), pp. 880–887.

ICML-2005-TannerS #network

TD(λ) networks: temporal-difference networks with eligibility traces (BT, RSS), pp. 888–895.

ICML-2005-TaskarCKG #approach #learning #modelling #predict #scalability

Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.

ICML-2005-ToussaintV #learning #modelling

Learning discontinuities with products-of-sigmoids for switching between local models (MT, SV), pp. 904–911.

ICML-2005-TsangKL #problem #scalability

Core Vector Regression for very large regression problems (IWT, JTK, KTL), pp. 912–919.

ICML-2005-Tsuda

Propagating distributions on a hypergraph by dual information regularization (KT), pp. 920–927.

ICML-2005-VeeramachaneniSA #classification #documentation

Hierarchical Dirichlet model for document classification (SV, DS, PA), pp. 928–935.

ICML-2005-WalderCS #modelling #problem

Implicit surface modelling as an eigenvalue problem (CW, OC, BS), pp. 936–939.

ICML-2005-WangS #classification #kernel

New kernels for protein structural motif discovery and function classification (CW, SDS), pp. 940–947.

ICML-2005-WangWGSC #markov #modelling #random #semantics

Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields (SW, SW, RG, DS, LC), pp. 948–955.

ICML-2005-WangLBS #online #optimisation

Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.

ICML-2005-Wiewiora #learning #predict

Learning predictive representations from a history (EW), pp. 964–971.

ICML-2005-WilliamsLXC #classification #using

Incomplete-data classification using logistic regression (DW, XL, YX, LC), pp. 972–979.

ICML-2005-WolfeJS #learning #predict

Learning predictive state representations in dynamical systems without reset (BW, MRJ, SPS), pp. 980–987.

ICML-2005-WuMR #classification #detection #linear #symmetry

Linear Asymmetric Classifier for cascade detectors (JW, MDM, JMR), pp. 988–995.

ICML-2005-WuSB #classification #scalability

Building Sparse Large Margin Classifiers (MW, BS, GHB), pp. 996–1003.

ICML-2005-XuTYYK #learning #relational

Dirichlet enhanced relational learning (ZX, VT, KY, SY, HPK), pp. 1004–1011.

ICML-2005-YuTS #learning #multi #process

Learning Gaussian processes from multiple tasks (KY, VT, AS), pp. 1012–1019.

ICML-2005-ZhangJS #naive bayes #ranking

Augmenting naive Bayes for ranking (HZ, LJ, JS), pp. 1020–1027.

ICML-2005-ZhouLZ #clustering #distance #metric

A new Mallows distance based metric for comparing clusterings (DZ, JL, HZ), pp. 1028–1035.

ICML-2005-ZhouHS #graph #learning

Learning from labeled and unlabeled data on a directed graph (DZ, JH, BS), pp. 1036–1043.

ICML-2005-ZhuNWZM #2d #information management #random #web

2D Conditional Random Fields for Web information extraction (JZ, ZN, JRW, BZ, WYM), pp. 1044–1051.

ICML-2005-ZhuL #graph #induction #learning #modelling #scalability

Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning (XZ, JDL), pp. 1052–1059.

ICML-2005-ZienC #scalability

Large margin non-linear embedding (AZ, JQC), pp. 1060–1067.