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* | 2008 | |
---|---|---|

108 | EE | Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers: Combining concept hierarchies and statistical topic models. CIKM 2008: 1469-1470 |

107 | EE | Chaitanya Chemudugunta, America Holloway, Padhraic Smyth, Mark Steyvers: Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning. International Semantic Web Conference 2008: 229-244 |

106 | EE | Ian Porteous, David Newman, Alexander T. Ihler, Arthur Asuncion, Padhraic Smyth, Max Welling: Fast collapsed gibbs sampling for latent dirichlet allocation. KDD 2008: 569-577 |

105 | EE | Arthur Asuncion, Padhraic Smyth, Max Welling: Asynchronous Distributed Learning of Topic Models. NIPS 2008: 81-88 |

104 | EE | Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers: Text Modeling using Unsupervised Topic Models and Concept Hierarchies CoRR abs/0808.0973: (2008) |

2007 | ||

103 | EE | Sergey Kirshner, Padhraic Smyth: Infinite mixtures of trees. ICML 2007: 417-423 |

102 | EE | David Newman, Kat Hagedorn, Chaitanya Chemudugunta, Padhraic Smyth: Subject metadata enrichment using statistical topic models. JCDL 2007: 366-375 |

101 | EE | David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling: Distributed Inference for Latent Dirichlet Allocation. NIPS 2007 |

100 | EE | James Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, Domonkos Tikk: KDD Cup and workshop 2007. SIGKDD Explorations 9(2): 51-52 (2007) |

99 | EE | Alexander T. Ihler, Jon Hutchins, Padhraic Smyth: Learning to detect events with Markov-modulated poisson processes. TKDD 1(3): (2007) |

2006 | ||

98 | EE | Padhraic Smyth: Data-Driven Discovery Using Probabilistic Hidden Variable Models. ALT 2006: 28 |

97 | EE | Padhraic Smyth: Data-Driven Discovery Using Probabilistic Hidden Variable Models. Discovery Science 2006: 13 |

96 | EE | David Newman, Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers: Analyzing Entities and Topics in News Articles Using Statistical Topic Models. ISI 2006: 93-104 |

95 | EE | Alexander T. Ihler, Jon Hutchins, Padhraic Smyth: Adaptive event detection with time-varying poisson processes. KDD 2006: 207-216 |

94 | EE | David Newman, Chaitanya Chemudugunta, Padhraic Smyth: Statistical entity-topic models. KDD 2006: 680-686 |

93 | EE | Seyoung Kim, Padhraic Smyth, Hal Stern: A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data. MICCAI (2) 2006: 217-224 |

92 | EE | Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers: Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. NIPS 2006: 241-248 |

91 | EE | Alexander T. Ihler, Padhraic Smyth: Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models. NIPS 2006: 625-632 |

90 | EE | Seyoung Kim, Padhraic Smyth: Hierarchical Dirichlet Processes with Random Effects. NIPS 2006: 697-704 |

89 | EE | Ian Porteous, Alex Ihter, Padhraic Smyth, Max Welling: Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. UAI 2006 |

88 | EE | Seyoung Kim, Padhraic Smyth: Segmental Hidden Markov Models with Random Effects for Waveform Modeling. Journal of Machine Learning Research 7: 945-969 (2006) |

2005 | ||

87 | EE | Seyoung Kim, Padhraic Smyth, Hal Stern, Jessica Turner: Parametric Response Surface Models for Analysis of Multi-site fMRI Data. MICCAI 2005: 352-359 |

86 | Scott White, Padhraic Smyth: A Spectral Clustering Approach To Finding Communities in Graph. SDM 2005 | |

85 | EE | Joshua O'Madadhain, Jon Hutchins, Padhraic Smyth: Prediction and ranking algorithms for event-based network data. SIGKDD Explorations 7(2): 23-30 (2005) |

2004 | ||

84 | EE | Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, Thomas L. Griffiths: Probabilistic author-topic models for information discovery. KDD 2004: 306-315 |

83 | EE | Scott Gaffney, Padhraic Smyth: Joint Probabilistic Curve Clustering and Alignment. NIPS 2004 |

82 | EE | Seyoung Kim, Padhraic Smyth, Stefan Luther: Modeling Waveform Shapes with Random E ects Segmental Hidden Markov Models. UAI 2004: 309-316 |

81 | EE | Sergey Kirshner, Padhraic Smyth, Andrew Robertson: Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. UAI 2004: 317-314 |

80 | EE | Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth: The Author-Topic Model for Authors and Documents. UAI 2004: 487-494 |

2003 | ||

79 | Pierre Baldi, Paolo Frasconi, Padhraic Smyth: Modeling the Internet and the Web: Probabilistic Method and Algorithms John Wiley 2003 | |

78 | Sergey Kirshner, Sridevi Parise, Padhraic Smyth: Unsupervised Learning with Permuted Data. ICML 2003: 345-352 | |

77 | EE | Scott White, Padhraic Smyth: Algorithms for estimating relative importance in networks. KDD 2003: 266-275 |

76 | EE | Darya Chudova, Scott Gaffney, Eric Mjolsness, Padhraic Smyth: Translation-invariant mixture models for curve clustering. KDD 2003: 79-88 |

75 | EE | Darya Chudova, Christopher Hart, Eric Mjolsness, Padhraic Smyth: Gene Expression Clustering with Functional Mixture Models. NIPS 2003 |

74 | EE | Dmitry Pavlov, Padhraic Smyth: Approximate Query Answering by Model Averaging. SDM 2003 |

73 | Darya Chudova, Scott Gaffney, Padhraic Smyth: Probabilistic Models For Joint Clustering And Time-Warping Of Multidimensional Curves. UAI 2003: 134-141 | |

72 | EE | Darya Chudova, Padhraic Smyth: Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework. Data Min. Knowl. Discov. 7(3): 273-299 (2003) |

71 | EE | 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) |

70 | EE | 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) |

2002 | ||

69 | EE | Padhraic Smyth: Learning with Mixture Models: Concepts and Applications. ECML 2002: 529- |

68 | EE | 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 |

67 | EE | Darya Chudova, Padhraic Smyth: Pattern discovery in sequences under a Markov assumption. KDD 2002: 153-162 |

66 | EE | Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath: Learning to Classify Galaxy Shapes Using the EM Algorithm. NIPS 2002: 1497-1504 |

65 | EE | Padhraic Smyth: Learning with Mixture Models: Concepts and Applications. PKDD 2002: 512 |

64 | EE | Padhraic Smyth, Daryl Pregibon, Christos Faloutsos: Data-driven evolution of data mining algorithms. Commun. ACM 45(8): 33-37 (2002) |

63 | EE | Chidanand Apté, Bing Liu, Edwin P. D. Pednault, Padhraic Smyth: Business applications of data mining. Commun. ACM 45(8): 49-53 (2002) |

62 | 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) | |

2001 | ||

61 | EE | Padhraic Smyth: Breaking out of the Black-Box: Research Challenges in Data Mining. DMKD 2001 |

60 | EE | Dmitry Pavlov, Padhraic Smyth: Probabilistic query models for transaction data. KDD 2001: 164-173 |

59 | EE | Igor V. Cadez, Padhraic Smyth, Heikki Mannila: Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. KDD 2001: 37-46 |

58 | EE | Igor V. Cadez, Padhraic Smyth: Bayesian Predictive Profiles With Applications to Retail Transaction Data. NIPS 2001: 1353-1360 |

57 | 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) | |

2000 | ||

56 | EE | Heikki Mannila, Padhraic Smyth: Approximate Query Answering with Frequent Sets and Maximum Entropy. ICDE 2000: 309 |

55 | EE | Igor V. Cadez, Scott Gaffney, Padhraic Smyth: A general probabilistic framework for clustering individuals and objects. KDD 2000: 140-149 |

54 | EE | 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 |

53 | EE | Dmitry Pavlov, Darya Chudova, Padhraic Smyth: Towards scalable support vector machines using squashing. KDD 2000: 295-299 |

52 | EE | Xianping Ge, Padhraic Smyth: Deformable Markov model templates for time-series pattern matching. KDD 2000: 81-90 |

51 | Igor V. Cadez, Padhraic Smyth: Model Complexity, Goodness of Fit and Diminishing Returns. NIPS 2000: 388-394 | |

50 | EE | Dmitry Pavlov, Heikki Mannila, Padhraic Smyth: Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. UAI 2000: 465-472 |

49 | EE | 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) |

1999 | ||

48 | Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan: Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999: 77-86 | |

47 | EE | Heikki Mannila, Dmitry Pavlov, Padhraic Smyth: Prediction with Local Patterns using Cross-Entropy. KDD 1999: 357-361 |

46 | EE | Scott Gaffney, Padhraic Smyth: Trajectory Clustering with Mixtures of Regression Models. KDD 1999: 63-72 |

45 | EE | Xianping Ge, Wanda Pratt, Padhraic Smyth: Discovering Chinese Words from Unsegmented Text (poster abstract). SIGIR 1999: 271-272 |

44 | EE | Xianping Ge, David Eppstein, Padhraic Smyth: The Distribution of Cycle Lengths in Graphical Models for Iterative Decoding CoRR cs.DM/9907002: (1999) |

43 | Padhraic Smyth, David Wolpert: Linearly Combining Density Estimators via Stacking. Machine Learning 36(1-2): 59-83 (1999) | |

1998 | ||

42 | Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, Padhraic Smyth: Rule Discovery from Time Series. KDD 1998: 16-22 | |

41 | 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) | |

1997 | ||

40 | 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 | |

39 | Eamonn J. Keogh, Padhraic Smyth: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. KDD 1997: 24-30 | |

38 | Padhraic Smyth, David Wolpert: Anytime Exploratory Data Analysis for Massive Data Sets. KDD 1997: 54-60 | |

37 | Padhraic Smyth, Michael Ghil, Kayo Ide, Joseph Roden, Andrew Fraser: Detecting Atmospheric Regimes Using Cross-Validated Clustering. KDD 1997: 61-66 | |

36 | Padhraic Smyth, David Wolpert: Stacked Density Estimation. NIPS 1997 | |

35 | Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth: Statistical Themes and Lessons for Data Mining. Data Min. Knowl. Discov. 1(1): 11-28 (1997) | |

34 | Pat Langley, Gregory M. Provan, Padhraic Smyth: Learning with Probabilistic Representations. Machine Learning 29(2-3): 91-101 (1997) | |

33 | EE | Padhraic Smyth, David Heckerman, Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9(2): 227-269 (1997) |

32 | EE | Padhraic Smyth: Belief networks, hidden Markov models, and Markov random fields: A unifying view. Pattern Recognition Letters 18(11-13): 1261-1268 (1997) |

1996 | ||

31 | Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, Ramasamy Uthurusamy: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996 | |

30 | Padhraic Smyth: Clustering Using Monte Carlo Cross-Validation. KDD 1996: 126-133 | |

29 | Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth: Knowledge Discovery and Data Mining: Towards a Unifying Framework. KDD 1996: 82-88 | |

28 | EE | Padhraic Smyth: Clustering Sequences with Hidden Markov Models. NIPS 1996: 648-654 |

27 | 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 | |

26 | 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 | |

25 | Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth: From Data Mining to Knowledge Discovery in Databases. AI Magazine 17(3): 37-54 (1996) | |

24 | EE | 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) |

23 | EE | Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth: Statistical Inference and Data Mining. Commun. ACM 39(11): 35-41 (1996) |

22 | EE | Padhraic Smyth: Bounds on the mean classification error rate of multiple experts. Pattern Recognition Letters 17(12): 1253-1257 (1996) |

1995 | ||

21 | Padhraic Smyth, Alexander Gray, Usama M. Fayyad: Retrofitting Decision Tree Classifiers Using Kernel Density Estimation. ICML 1995: 506-514 | |

20 | 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) | |

1994 | ||

19 | Usama M. Fayyad, Padhraic Smyth: The Automated Analysis, Cataloging, and Searching of Digital Image Libraries: A Machine Learning Approach. DL 1994: 225-249 | |

18 | 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 | |

17 | 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 | |

16 | EE | 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 |

15 | 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) | |

14 | EE | Padhraic Smyth: Hidden Markov models for fault detection in dynamic system. Pattern Recognition 27(1): 149-164 (1994) |

1993 | ||

13 | EE | Padhraic Smyth: Probabilistic Anomaly Detection in Dynamic Systems. NIPS 1993: 825-832 |

12 | 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) | |

11 | EE | Zheng Zeng, Rodney M. Goodman, Padhraic Smyth: Learning Finite State Machines With Self-Clustering Recurrent Networks. Neural Computation 5(6): 976-990 (1993) |

1992 | ||

10 | Padhraic Smyth, Jeff Mellstrom: Detecting Novel Classes with Applications to Fault Diagnosis. ML 1992: 416-425 | |

9 | EE | Padhraic Smyth, Rodney M. Goodman: An Information Theoretic Approach to Rule Induction from Databases. IEEE Trans. Knowl. Data Eng. 4(4): 301-316 (1992) |

1991 | ||

8 | EE | Padhraic Smyth, Jeff Mellstrom: Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models. NIPS 1991: 667-674 |

7 | Padhraic Smyth, Rodney M. Goodman: Rule Induction Using Information Theory. Knowledge Discovery in Databases 1991: 159-176 | |

1990 | ||

6 | Padhraic Smyth, Rodney M. Goodman, Charles M. Higgins: A Hybrid Rule-Based/Bayesian Classifier. ECAI 1990: 610-615 | |

5 | EE | Padhraic Smyth: On Stochastic Complexity and Admissible Models for Neural Network Classifiers. NIPS 1990: 818-824 |

1989 | ||

4 | Rodney M. Goodman, Padhraic Smyth: The Induction of Probabilistic Rule Sets - The Itrule Algorithm. ML 1989: 129-132 | |

1988 | ||

3 | Rodney M. Goodman, Padhraic Smyth: Information-Theoretic Rule Induction. ECAI 1988: 357-362 | |

2 | EE | Rodney M. Goodman, John W. Miller, Padhraic Smyth: An Information Theoretic Approach to Rule-Based Connectionist Expert Systems. NIPS 1988: 256-263 |

1 | Rodney M. Goodman, Padhraic Smyth: Decision tree design from a communication theory standpoint. IEEE Transactions on Information Theory 34(5): 979-994 (1988) |