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Note: Links lead to the DBLP on the Web. Andrew W. Moore Paul Komarek , Andrew W. Moore: Making Logistic Regression a Core Data Mining Tool with TR-IRLS. ICDM 2005 : 685-688 Sajid M. Siddiqi , Andrew W. Moore: Fast inference and learning in large-state-space HMMs. ICML 2005 : 800-807 Jeremy Kubica , Andrew W. Moore, Andrew Connolly , Robert Jedicke : A multiple tree algorithm for the efficient association of asteroid observations. KDD 2005 : 138-146 Daniel B. Neill , Andrew W. Moore, Maheshkumar Sabhnani , Kenny Daniel : Detection of emerging space-time clusters. KDD 2005 : 218-227 Daniel B. Neill , Andrew W. Moore, Gregory F. Cooper : A Bayesian Spatial Scan Statistic. NIPS 2005 Dongryeol Lee , Alexander G. Gray , Andrew W. Moore: Dual-Tree Fast Gauss Transforms. NIPS 2005 Purnamrita Sarkar , Andrew W. Moore: Dynamic Social Network Analysis using Latent Space Models. NIPS 2005 Brigham Anderson , Andrew W. Moore: Fast Information Value for Graphical Models. NIPS 2005 Jeremy Kubica , Joseph Masiero , Andrew W. Moore, Robert Jedicke , Andrew Connolly : Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery. NIPS 2005 Denis Zuev , Andrew W. Moore: Traffic Classification Using a Statistical Approach. PAM 2005 : 321-324 Andrew W. Moore, Konstantina Papagiannaki : Toward the Accurate Identification of Network Applications. PAM 2005 : 41-54 Andrew W. Moore, Denis Zuev : Internet traffic classification using bayesian analysis techniques. SIGMETRICS 2005 : 50-60 Purnamrita Sarkar , Andrew W. Moore: Dynamic social network analysis using latent space models. SIGKDD Explorations 7 (2): 31-40 (2005) Daniel B. Neill , Andrew W. Moore: Rapid detection of significant spatial clusters. KDD 2004 : 256-265 Brigham Anderson , Andrew W. Moore, Andrew Connolly , Robert Nichol : Fast nonlinear regression via eigenimages applied to galactic morphology. KDD 2004 : 40-48 Kaustav Das , Andrew W. Moore, Jeff G. Schneider : Belief state approaches to signaling alarms in surveillance systems. KDD 2004 : 539-544 Ting Liu , Ke Yang , Andrew W. Moore: The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data. KDD 2004 : 629-634 Dan Pelleg , Andrew W. Moore: Active Learning for Anomaly and Rare-Category Detection. NIPS 2004 Ting Liu , Andrew W. Moore, Alexander G. Gray , Ke Yang : An Investigation of Practical Approximate Nearest Neighbor Algorithms. NIPS 2004 Daniel B. Neill , Andrew W. Moore, Francisco Pereira , Tom M. Mitchell : Detecting Significant Multidimensional Spatial Clusters. NIPS 2004 Andrew W. Moore: An implementation-based comparison of Measurement-Based Admission Control algorithms. J. High Speed Networks 13 (2): 87-102 (2004) Jeremy Kubica , Andrew W. Moore: Probabilistic Noise Identification and Data Cleaning. ICDM 2003 : 131-138 Jeremy Kubica , Andrew W. Moore, Jeff G. Schneider : Tractable Group Detection on Large Link Data Sets. ICDM 2003 : 573-576 Jeremy Kubica , Andrew W. Moore, David Cohn , Jeff G. Schneider : Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries. ICML 2003 : 392-399 Andrew W. Moore, Weng-Keen Wong : Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning. ICML 2003 : 552-559 Weng-Keen Wong , Andrew W. Moore, Gregory F. Cooper , Michael Wagner : Bayesian Network Anomaly Pattern Detection for Disease Outbreaks. ICML 2003 : 808-815 Daniel B. Neill , Andrew W. Moore: A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters. NIPS 2003 Ting Liu , Andrew W. Moore, Alexander G. Gray : Efficient Exact k-NN and Nonparametric Classification in High Dimensions. NIPS 2003 Alexander G. Gray , Andrew W. Moore: Nonparametric Density Estimation: Toward Computational Tractability. SDM 2003 Weng-Keen Wong , Andrew W. Moore, Gregory F. Cooper , Michael Wagner : Rule-Based Anomaly Pattern Detection for Detecting Disease Outbreaks. AAAI/IAAI 2002 : 217-223 Jeremy Kubica , Andrew W. Moore, Jeff G. Schneider , Yiming Yang : Stochastic Link and Group Detection. AAAI/IAAI 2002 : 798- Amitabh Chaudhary , Alexander S. Szalay , Andrew W. Moore: Very Fast Outlier Detection in Large Multidimensional Data Sets. DMKD 2002 Dan Pelleg , Andrew W. Moore: Using Tarjan's Red Rule for Fast Dependency Tree Construction. NIPS 2002 : 801-808 Scott Davies , Andrew W. Moore: Interpolating Conditional Density Trees. UAI 2002 : 119-127 Andrew W. Moore, Jeff G. Schneider : Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes. UAI 2002 : 360-369 Malcolm J. A. Strens , Andrew W. Moore: Policy Search using Paired Comparisons. Journal of Machine Learning Research 3 : 921-950 (2002) Rémi Munos , Andrew W. Moore: Variable Resolution Discretization in Optimal Control. Machine Learning 49 (2-3): 291-323 (2002) Dan Pelleg , Andrew W. Moore: Mixtures of Rectangles: Interpretable Soft Clustering. ICML 2001 : 401-408 Peter Sand , Andrew W. Moore: Repairing Faulty Mixture Models using Density Estimation. ICML 2001 : 457-464 Malcolm J. A. Strens , Andrew W. Moore: Direct Policy Search using Paired Statistical Tests. ICML 2001 : 545-552 Martin A. Riedmiller , Andrew W. Moore, Jeff G. Schneider : Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids. Balancing Reactivity and Social Deliberation in Multi-Agent Systems 2000 : 137-149 Brigham S. Anderson , Andrew W. Moore, David Cohn : A Nonparametric Approach to Noisy and Costly Optimization. ICML 2000 : 17-24 Paul Komarek , Andrew W. Moore: A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets. ICML 2000 : 495-502 Rémi Munos , Andrew W. Moore: Rates of Convergence for Variable Resolution Schemes in Optimal Control. ICML 2000 : 647-654 Dan Pelleg , Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. ICML 2000 : 727-734 Andrew W. Moore, Jeff G. Schneider , Justin A. Boyan , Mary S. Lee : Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICRA 2000 : 4096- Alexander G. Gray , Andrew W. Moore: `N-Body' Problems in Statistical Learning. NIPS 2000 : 521-527 Scott Davies , Andrew W. Moore: Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous And Discrete Variables. UAI 2000 : 168-175 Andrew W. Moore: The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data. UAI 2000 : 397-405 Justin A. Boyan , Andrew W. Moore: Learning Evaluation Functions to Improve Optimization by Local Search. Journal of Machine Learning Research 1 : 77-112 (2000) Jeff G. Schneider , Weng-Keen Wong , Andrew W. Moore, Martin A. Riedmiller : Distributed Value Functions. ICML 1999 : 371-378 Andrew W. Moore, Leemon C. Baird III , Leslie Pack Kaelbling : Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs. IJCAI 1999 : 1316-1323 Rémi Munos , Andrew W. Moore: Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems. IJCAI 1999 : 1348-1355 Dan Pelleg , Andrew W. Moore: Accelerating Exact k -means Algorithms with Geometric Reasoning. KDD 1999 : 277-281 Scott Davies , Andrew W. Moore: Bayesian Networks for Lossless Dataset Compression. KDD 1999 : 387-391 Justin A. Boyan , Andrew W. Moore: Learning Evaluation Functions for Global Optimization and Boolean Satisfiability. AAAI/IAAI 1998 : 3-10 Andrew W. Moore, Jeff G. Schneider , Justin A. Boyan , Mary S. Lee : Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICML 1998 : 386-394 Jeff G. Schneider , Justin A. Boyan , Andrew W. Moore: Value Function Based Production Scheduling. ICML 1998 : 522-530 Brigham S. Anderson , Andrew W. Moore: ADtrees for Fast Counting and for Fast Learning of Association Rules. KDD 1998 : 134-138 Rémi Munos , Andrew W. Moore: Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. NIPS 1998 : 1024-1030 Andrew W. Moore: Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees. NIPS 1998 : 543-549 Leemon C. Baird III , Andrew W. Moore: Gradient Descent for General Reinforcement Learning. NIPS 1998 : 968-974 Andrew W. Moore, Mary S. Lee : Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets CoRR cs.AI/9803102 : (1998) Andrew W. Moore, Mary S. Lee : Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets. J. Artif. Intell. Res. (JAIR) 8 : 67-91 (1998) Andrew W. Moore, Jeff G. Schneider , Kan Deng : Efficient Locally Weighted Polynomial Regression Predictions. ICML 1997 : 236-244 Christopher G. Atkeson , Andrew W. Moore, Stefan Schaal : Locally Weighted Learning. Artif. Intell. Rev. 11 (1-5): 11-73 (1997) Oded Maron , Andrew W. Moore: The Racing Algorithm: Model Selection for Lazy Learners. Artif. Intell. Rev. 11 (1-5): 193-225 (1997) Christopher G. Atkeson , Andrew W. Moore, Stefan Schaal : Locally Weighted Learning for Control. Artif. Intell. Rev. 11 (1-5): 75-113 (1997) Andrew W. Moore: Reinforcement Learning in Factories: The Auton Project (Abstract). ICML 1996 : 556 Justin A. Boyan , Andrew W. Moore: Learning Evaluation Functions for Large Acyclic Domains. ICML 1996 : 63-70 Leslie Pack Kaelbling , Michael L. Littman , Andrew W. Moore: Reinforcement Learning: A Survey CoRR cs.AI/9605103 : (1996) Kan Deng , Andrew W. Moore: Multiresolution Instance-Based Learning. IJCAI 1995 : 1233-1242 Andrew W. Moore, Jeff G. Schneider : Memory-based Stochastic Optimization. NIPS 1995 : 1066-1072 Andrew W. Moore, Christopher G. Atkeson : The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces. Machine Learning 21 (3): 199-233 (1995) Andrew W. Moore, Mary S. Lee : Efficient Algorithms for Minimizing Cross Validation Error. ICML 1994 : 190-198 Justin A. Boyan , Andrew W. Moore: Generalization in Reinforcement Learning: Safely Approximating the Value Function. NIPS 1994 : 369-376 Thomas G. Dietterich , Dietrich Wettschereck , Christopher G. Atkeson , Andrew W. Moore: Memory-Based Methods for Regression and Classification. NIPS 1993 : 1165-1166 Oded Maron , Andrew W. Moore: Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation. NIPS 1993 : 59-66 Andrew W. Moore: The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces. NIPS 1993 : 711-718 Andrew W. Moore, Christopher G. Atkeson : Prioritized Sweeping: Reinforcement Learning With Less Data and Less Time. Machine Learning 13 : 103-130 (1993) Andrew W. Moore, Christopher G. Atkeson : Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping. NIPS 1992 : 263-270 Andrew W. Moore: Variable Resolution Dynamic Programming. ML 1991 : 333-337 Andrew W. Moore: Fast, Robust Adaptive Control by Learning only Forward Models. NIPS 1991 : 571-578 Andrew W. Moore: Acquisition of Dynamic Control Knowledge for a Robotic Manipulator. 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