| |
 |      |
                                               |
Note: Links lead to the DBLP on the Web. Padhraic Smyth David Newman , Chaitanya Chemudugunta , Padhraic Smyth, Mark Steyvers : Analyzing Entities and Topics in News Articles Using Statistical Topic Models. ISI 2006 : 93-104 Seyoung Kim , Padhraic Smyth, Hal Stern , Jessica Turner : Parametric Response Surface Models for Analysis of Multi-site fMRI Data. MICCAI 2005 : 352-359 Scott White , Padhraic Smyth: A Spectral Clustering Approach To Finding Communities in Graph. SDM 2005 Joshua O'Madadhain , Jon Hutchins , Padhraic Smyth: Prediction and ranking algorithms for event-based network data. SIGKDD Explorations 7 (2): 23-30 (2005) Mark Steyvers , Padhraic Smyth, Michal Rosen-Zvi , Thomas L. Griffiths : Probabilistic author-topic models for information discovery. KDD 2004 : 306-315 Scott Gaffney , Padhraic Smyth: Joint Probabilistic Curve Clustering and Alignment. NIPS 2004 Pierre Baldi , Paolo Frasconi , Padhraic Smyth: Modeling the Internet and the Web: Probabilistic Method and Algorithms John Wiley 2003 Sergey Kirshner , Sridevi Parise , Padhraic Smyth: Unsupervised Learning with Permuted Data. ICML 2003 : 345-352 Scott White , Padhraic Smyth: Algorithms for estimating relative importance in networks. KDD 2003 : 266-275 Darya Chudova , Scott Gaffney , Eric Mjolsness , Padhraic Smyth: Translation-invariant mixture models for curve clustering. KDD 2003 : 79-88 Darya Chudova , Christopher Hart , Eric Mjolsness , Padhraic Smyth: Gene Expression Clustering with Functional Mixture Models. NIPS 2003 Dmitry Pavlov , Padhraic Smyth: Approximate Query Answering by Model Averaging. SDM 2003 Darya Chudova , Scott Gaffney , Padhraic Smyth: Probabilistic Models For Joint Clustering And Time-Warping Of Multidimensional Curves. UAI 2003 : 134-141 Darya Chudova , Padhraic Smyth: Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework. Data Min. Knowl. Discov. 7 (3): 273-299 (2003) Igor V. Cadez , David Heckerman , Christopher Meek , Padhraic Smyth, Steven White : Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. Data Min. Knowl. Discov. 7 (4): 399-424 (2003) Dmitry Pavlov , Heikki Mannila , Padhraic Smyth: Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data. IEEE Trans. Knowl. Data Eng. 15 (6): 1409-1421 (2003) Padhraic Smyth: Learning with Mixture Models: Concepts and Applications. ECML 2002 : 529- Sergey Kirshner , Igor V. Cadez , Padhraic Smyth, Chandrika Kamath , Erick Cantú-Paz : Probabilistic Model-Based Detection of Bent-Double Radio Galaxies. ICPR (2) 2002 : 499-502 Darya Chudova , Padhraic Smyth: Pattern discovery in sequences under a Markov assumption. KDD 2002 : 153-162 Sergey Kirshner , Igor V. Cadez , Padhraic Smyth, Chandrika Kamath : Learning to Classify Galaxy Shapes Using the EM Algorithm. NIPS 2002 : 1497-1504 Padhraic Smyth: Learning with Mixture Models: Concepts and Applications. PKDD 2002 : 512 Padhraic Smyth, Daryl Pregibon , Christos Faloutsos : Data-driven evolution of data mining algorithms. Commun. ACM 45 (8): 33-37 (2002) Chidanand Apté , Bing Liu , Edwin P. D. Pednault , Padhraic Smyth: Business applications of data mining. Commun. ACM 45 (8): 49-53 (2002) Igor V. Cadez , Padhraic Smyth, Geoffrey J. McLachlan , Christine E. McLaren : Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Machine Learning 47 (1): 7-34 (2002) Padhraic Smyth: Breaking out of the Black-Box: Research Challenges in Data Mining. DMKD 2001 Dmitry Pavlov , Padhraic Smyth: Probabilistic query models for transaction data. KDD 2001 : 164-173 Igor V. Cadez , Padhraic Smyth, Heikki Mannila : Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. KDD 2001 : 37-46 Igor V. Cadez , Padhraic Smyth: Bayesian Predictive Profiles With Applications to Retail Transaction Data. NIPS 2001 : 1353-1360 Xianping Ge , David Eppstein , Padhraic Smyth: The distribution of loop lengths in graphical models for turbo decoding. IEEE Transactions on Information Theory 47 (6): 2549-2553 (2001) Heikki Mannila , Padhraic Smyth: Approximate Query Answering with Frequent Sets and Maximum Entropy. ICDE 2000 : 309 Igor V. Cadez , Scott Gaffney , Padhraic Smyth: A general probabilistic framework for clustering individuals and objects. KDD 2000 : 140-149 Igor V. Cadez , David Heckerman , Christopher Meek , Padhraic Smyth, Steven White : Visualization of navigation patterns on a Web site using model-based clustering. KDD 2000 : 280-284 Dmitry Pavlov , Darya Chudova , Padhraic Smyth: Towards scalable support vector machines using squashing. KDD 2000 : 295-299 Xianping Ge , Padhraic Smyth: Deformable Markov model templates for time-series pattern matching. KDD 2000 : 81-90 Igor V. Cadez , Padhraic Smyth: Model Complexity, Goodness of Fit and Diminishing Returns. NIPS 2000 : 388-394 Dmitry Pavlov , Heikki Mannila , Padhraic Smyth: Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. UAI 2000 : 465-472 Stephen D. Bay , Dennis F. Kibler , Michael J. Pazzani , Padhraic Smyth: The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. SIGKDD Explorations 2 (2): 81-85 (2000) Igor V. Cadez , Christine E. McLaren , Padhraic Smyth, Geoffrey J. McLachlan : Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999 : 77-86 Heikki Mannila , Dmitry Pavlov , Padhraic Smyth: Prediction with Local Patterns using Cross-Entropy. KDD 1999 : 357-361 Scott Gaffney , Padhraic Smyth: Trajectory Clustering with Mixtures of Regression Models. KDD 1999 : 63-72 Xianping Ge , Wanda Pratt , Padhraic Smyth: Discovering Chinese Words from Unsegmented Text (poster abstract). SIGIR 1999 : 271-272 Xianping Ge , David Eppstein , Padhraic Smyth: The Distribution of Cycle Lengths in Graphical Models for Iterative Decoding CoRR cs.DM/9907002 : (1999) Padhraic Smyth, David Wolpert : Linearly Combining Density Estimators via Stacking. Machine Learning 36 (1-2): 59-83 (1999) Gautam Das , King-Ip Lin , Heikki Mannila , Gopal Renganathan , Padhraic Smyth: Rule Discovery from Time Series. KDD 1998 : 16-22 Michael C. Burl , Lars Asker , Padhraic Smyth, Usama M. Fayyad , Pietro Perona , Larry Crumpler , Jayne Aubele : Learning to Recognize Volcanoes on Venus. Machine Learning 30 (2-3): 165-194 (1998) William Rodman Shankle , Subramani Mani , Michael J. Pazzani , Padhraic Smyth: Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. AIME 1997 : 73-85 Eamonn J. Keogh , Padhraic Smyth: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. KDD 1997 : 24-30 Padhraic Smyth, David Wolpert : Anytime Exploratory Data Analysis for Massive Data Sets. KDD 1997 : 54-60 Padhraic Smyth, Michael Ghil , Kayo Ide , Joe Roden , Andrew Fraser : Detecting Atmospheric Regimes Using Cross-Validated Clustering. KDD 1997 : 61-66 Padhraic Smyth, David Wolpert : Stacked Density Estimation. NIPS 1997 Clark Glymour , David Madigan , Daryl Pregibon , Padhraic Smyth: Statistical Themes and Lessons for Data Mining. Data Min. Knowl. Discov. 1 (1): 11-28 (1997) Pat Langley , Gregory M. Provan , Padhraic Smyth: Learning with Probabilistic Representations. Machine Learning 29 (2-3): 91-101 (1997) Padhraic Smyth, David Heckerman , Michael I. Jordan : Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9 (2): 227-269 (1997) Padhraic Smyth: Belief networks, hidden Markov models, and Markov random fields: A unifying view. Pattern Recognition Letters 18 (11-13): 1261-1268 (1997) Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth, Ramasamy Uthurusamy : Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996 Padhraic Smyth: Clustering Using Monte Carlo Cross-Validation. KDD 1996 : 126-133 Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth: Knowledge Discovery and Data Mining: Towards a Unifying Framework. KDD 1996 : 82-88 Padhraic Smyth: Clustering Sequences with Hidden Markov Models. NIPS 1996 : 648-654 Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth: From Data Mining to Knowledge Discovery: An Overview. Advances in Knowledge Discovery and Data Mining 1996 : 1-34 Padhraic Smyth, Usama M. Fayyad , Michael C. Burl , Pietro Perona : Modeling Subjective Uncertainty in Image Annotation. Advances in Knowledge Discovery and Data Mining 1996 : 517-539 Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth: From Data Mining to Knowledge Discovery in Databases. AI Magazine 17 (3): 37-54 (1996) Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM 39 (11): 27-34 (1996) Clark Glymour , David Madigan , Daryl Pregibon , Padhraic Smyth: Statistical Inference and Data Mining. Commun. ACM 39 (11): 35-41 (1996) Padhraic Smyth: Bounds on the mean classification error rate of multiple experts. Pattern Recognition Letters 17 (12): 1253-1257 (1996) Padhraic Smyth, Alexander Gray , Usama M. Fayyad : Retrofitting Decision Tree Classifiers Using Kernel Density Estimation. ICML 1995 : 506-514 Usama M. Fayyad , Padhraic Smyth, Nicholas Weir , S. George Djorgovski : Automated Analysis and Exploration of Image Databases: Results, Progress, and Challenges. J. Intell. Inf. Syst. 4 (1): 7-25 (1995) Usama M. Fayyad , Padhraic Smyth: The Automated Analysis, Cataloging, and Searching of Digital Image Libraries: A Machine Learning Approach. DL 1994 : 225-249 Michael C. Burl , Usama M. Fayyad , Pietro Perona , Padhraic Smyth: Automated Analysis of Radar Imagery of Venus: Handling Lack of Ground Truth. ICIP (3) 1994 : 236-240 Padhraic Smyth, Michael C. Burl , Usama M. Fayyad , Pietro Perona : Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth. KDD Workshop 1994 : 109-120 Padhraic Smyth, Usama M. Fayyad , Michael C. Burl , Pietro Perona , Pierre Baldi : Inferring Ground Truth from Subjective Labelling of Venus Images. NIPS 1994 : 1085-1092 Gregory Piatetsky-Shapiro , Christopher J. Matheus , Padhraic Smyth, Ramasamy Uthurusamy : KDD-93: Progress and Challenges in Knowledge Discovery in Databases. AI Magazine 15 (3): 77-82 (1994) Padhraic Smyth: Hidden Markov models for fault detection in dynamic system. Pattern Recognition 27 (1): 149-164 (1994) Padhraic Smyth: Probabilistic Anomaly Detection in Dynamic Systems. NIPS 1993 : 825-832 John W. Miller , Rodney M. Goodman , Padhraic Smyth: On loss functions which minimize to conditional expected values and posterior proba- bilities. IEEE Transactions on Information Theory 39 (4): 1404- (1993) Padhraic Smyth, Jeff Mellstrom : Detecting Novel Classes with Applications to Fault Diagnosis. ML 1992 : 416-425 Padhraic Smyth, Rodney M. Goodman : An Information Theoretic Approach to Rule Induction from Databases. IEEE Trans. Knowl. Data Eng. 4 (4): 301-316 (1992) Padhraic Smyth, Jeff Mellstrom : Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models. NIPS 1991 : 667-674 Padhraic Smyth, Rodney M. Goodman : Rule Induction Using Information Theory. Knowledge Discovery in Databases 1991 : 159-176 Padhraic Smyth, Rodney M. Goodman , C. Higgins : A Hybrid Rule-Based/Bayesian Classifier. ECAI 1990 : 610-615 Padhraic Smyth: On Stochastic Complexity and Admissible Models for Neural Network Classifiers. NIPS 1990 : 818-824 Rodney M. Goodman , Padhraic Smyth: The Induction of Probabilistic Rule Sets - The Itrule Algorithm. ML 1989 : 129-132 Rodney M. Goodman , Padhraic Smyth: Information-Theoretic Rule Induction. ECAI 1988 : 357-362 Rodney M. Goodman , John W. Miller , Padhraic Smyth: An Information Theoretic Approach to Rule-Based Connectionist Expert Systems. NIPS 1988 : 256-263 Rodney M. Goodman , Padhraic Smyth: Decision tree design from a communication theory standpoint. IEEE Transactions on Information Theory 34 (5): 979-994 (1988) 1 [ 62 ] 2 [ 40 ] 3 [ 40 ] 4 [ 15 ] [ 78 ] 5 [ 48 ] 6 [ 15 ] [ 16 ] [ 17 ] [ 25 ] [ 40 ] 7 [ 47 ] [ 50 ] [ 53 ] [ 54 ] [ 57 ] [ 58 ] [ 61 ] [ 65 ] [ 67 ] [ 70 ] 8 [ 67 ] 9 [ 84 ] 10 [ 52 ] [ 66 ] [ 71 ] [ 72 ] [ 74 ] [ 75 ] 11 [ 40 ] 12 [ 41 ] 13 [ 19 ] 14 [ 43 ] [ 56 ] 15 [ 63 ] 16 [ 15 ] [ 16 ] [ 17 ] [ 18 ] [ 19 ] [ 20 ] [ 23 ] [ 24 ] [ 25 ] [ 26 ] [ 28 ] [ 30 ] [ 40 ] 17 [ 78 ] 18 [ 36 ] 19 [ 45 ] [ 54 ] [ 72 ] [ 75 ] [ 79 ] 20 [ 43 ] [ 44 ] [ 51 ] [ 56 ] 21 [ 36 ] 22 [ 22 ] [ 34 ] 23 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 6 ] [ 7 ] [ 9 ] [ 11 ] 24 [ 20 ] 25 [ 80 ] 26 [ 74 ] 27 [ 32 ] [ 53 ] [ 70 ] 28 [ 6 ] 29 [ 81 ] 30 [ 36 ] 31 [ 32 ] 32 [ 65 ] [ 67 ] 33 [ 38 ] 34 [ 48 ] 35 [ 83 ] 36 [ 65 ] [ 67 ] [ 77 ] 37 [ 33 ] 38 [ 41 ] 39 [ 62 ] 40 [ 22 ] [ 34 ] 41 [ 39 ] 42 [ 41 ] [ 46 ] [ 49 ] [ 55 ] [ 58 ] [ 69 ] 43 [ 14 ] 44 [ 47 ] [ 61 ] 45 [ 47 ] [ 61 ] 46 [ 53 ] [ 70 ] 47 [ 8 ] [ 10 ] 48 [ 2 ] [ 11 ] 49 [ 74 ] [ 75 ] 50 [ 84 ] 51 [ 81 ] 52 [ 77 ] 53 [ 46 ] [ 49 ] [ 52 ] [ 59 ] [ 69 ] [ 73 ] 54 [ 39 ] [ 48 ] 55 [ 62 ] 56 [ 15 ] [ 16 ] [ 17 ] [ 25 ] [ 40 ] 57 [ 14 ] [ 23 ] [ 24 ] [ 26 ] [ 28 ] [ 30 ] 58 [ 44 ] 59 [ 22 ] [ 34 ] [ 63 ] 60 [ 33 ] 61 [ 41 ] 62 [ 36 ] 63 [ 80 ] 64 [ 39 ] 65 [ 83 ] 66 [ 80 ] [ 84 ] 67 [ 83 ] 68 [ 14 ] [ 30 ] 69 [ 19 ] 70 [ 76 ] [ 82 ] 71 [ 53 ] [ 70 ] 72 [ 35 ] [ 37 ] [ 42 ]  ©2006 Association for Computing Machinery |
