Travelled to:1 × Canada
1 × Finland
1 × Germany
1 × Italy
1 × Slovenia
14 × USA
Collaborated with:J.G.Schneider D.Pelleg J.A.Boyan W.Wong ∅ P.Sarkar B.S.Anderson D.B.Neill M.S.Lee D.Cohn J.Roure S.M.Siddiqi J.Kubica A.J.Connolly A.Goldenberg P.Sand M.J.A.Strens P.Komarek R.Munos S.Davies A.Prakash K.Das T.Liu K.Yang K.Deng R.Jedicke M.Sabhnani K.Daniel B.Anderson R.Nichol G.F.Cooper M.M.Wagner M.A.Riedmiller
Talks about:learn (7) fast (7) effici (6) larg (5) algorithm (4) function (4) cluster (4) search (4) optim (4) bayesian (3)
Person: Andrew W. Moore
DBLP: Moore:Andrew_W=
Contributed to:
2010
200820062005200520042004200320012000199919991998199819971996199419911990Wrote 32 papers:
- KDD-2010-SarkarM #graph #nearest neighbour #performance
- Fast nearest-neighbor search in disk-resident graphs (PS, AWM), pp. 513–522.
- ICML-2008-SarkarMP #graph #incremental #performance #proximity #scalability
- Fast incremental proximity search in large graphs (PS, AWM, AP), pp. 896–903.
- ICML-2006-RoureM
- Sequential update of ADtrees (JR, AWM), pp. 769–776.
- ICML-2005-SiddiqiM #learning #performance
- Fast inference and learning in large-state-space HMMs (SMS, AWM), pp. 800–807.
- KDD-2005-KubicaMCJ #algorithm #multi #performance
- A multiple tree algorithm for the efficient association of asteroid observations (JK, AWM, AJC, RJ), pp. 138–146.
- KDD-2005-NeillMSD #clustering #detection
- Detection of emerging space-time clusters (DBN, AWM, MS, KD), pp. 218–227.
- ICML-2004-GoldenbergM #learning #scalability
- Tractable learning of large Bayes net structures from sparse data (AG, AWM).
- KDD-2004-AndersonMCN #performance
- Fast nonlinear regression via eigenimages applied to galactic morphology (BA, AWM, AJC, RN), pp. 40–48.
- KDD-2004-DasMS
- Belief state approaches to signaling alarms in surveillance systems (KD, AWM, JGS), pp. 539–544.
- KDD-2004-LiuYM #algorithm #classification #parametricity #performance
- The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data (TL, KY, AWM), pp. 629–634.
- KDD-2004-NeillM #agile #clustering #detection
- Rapid detection of significant spatial clusters (DBN, AWM), pp. 256–265.
- ICML-2003-KubicaMCS #analysis #collaboration #graph #performance #query
- Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries (JK, AWM, DC, JGS), pp. 392–399.
- ICML-2003-MooreW #learning #network
- Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning (AWM, WKW), pp. 552–559.
- ICML-2003-WongMCW #detection #network
- Bayesian Network Anomaly Pattern Detection for Disease Outbreaks (WKW, AWM, GFC, MMW), pp. 808–815.
- ICML-2001-PellegM #clustering
- Mixtures of Rectangles: Interpretable Soft Clustering (DP, AWM), pp. 401–408.
- ICML-2001-SandM #estimation #modelling #using
- Repairing Faulty Mixture Models using Density Estimation (PS, AWM), pp. 457–464.
- ICML-2001-StrensM #policy #statistics #testing #using
- Direct Policy Search using Paired Statistical Tests (MJAS, AWM), pp. 545–552.
- ICML-2000-AndersonMC #approach #optimisation #parametricity
- A Nonparametric Approach to Noisy and Costly Optimization (BSA, AWM, DC), pp. 17–24.
- ICML-2000-KomarekM #adaptation #machine learning #performance #scalability #set
- A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets (PK, AWM), pp. 495–502.
- ICML-2000-MunosM #convergence
- Rates of Convergence for Variable Resolution Schemes in Optimal Control (RM, AWM), pp. 647–654.
- ICML-2000-PellegM #clustering #estimation #named #performance
- X-means: Extending K-means with Efficient Estimation of the Number of Clusters (DP, AWM), pp. 727–734.
- ICML-1999-SchneiderWMR #distributed
- Distributed Value Functions (JGS, WKW, AWM, MAR), pp. 371–378.
- KDD-1999-DaviesM #dataset #network
- Bayesian Networks for Lossless Dataset Compression (SD, AWM), pp. 387–391.
- KDD-1999-PellegM #algorithm #geometry #reasoning
- Accelerating Exact k-means Algorithms with Geometric Reasoning (DP, AWM), pp. 277–281.
- ICML-1998-MooreSBL #learning #named #optimisation
- Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.
- ICML-1998-SchneiderBM #scheduling
- Value Function Based Production Scheduling (JGS, JAB, AWM), pp. 522–530.
- KDD-1998-AndersonM #learning #performance
- ADtrees for Fast Counting and for Fast Learning of Association Rules (BSA, AWM), pp. 134–138.
- ICML-1997-MooreSD #performance #polynomial #predict
- Efficient Locally Weighted Polynomial Regression Predictions (AWM, JGS, KD), pp. 236–244.
- ICML-1996-BoyanM #evaluation #learning #scalability
- Learning Evaluation Functions for Large Acyclic Domains (JAB, AWM), pp. 63–70.
- ICML-1994-MooreL #algorithm #fault #performance #validation
- Efficient Algorithms for Minimizing Cross Validation Error (AWM, MSL), pp. 190–198.
- ML-1991-Moore #programming
- Variable Resolution Dynamic Programming (AWM), pp. 333–337.
- ML-1990-Moore
- Acquisition of Dynamic Control Knowledge for a Robotic Manipulator (AWM), pp. 244–252.