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Padhraic Smyth: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Padhraic Smyth, Rodney M. Goodman
    An Information Theoretic Approach to Rule Induction from Databases. [Citation Graph (6, 13)][DBLP]
    IEEE Trans. Knowl. Data Eng., 1992, v:4, n:4, pp:301-316 [Journal]
  2. Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth
    Knowledge Discovery and Data Mining: Towards a Unifying Framework. [Citation Graph (3, 0)][DBLP]
    KDD, 1996, pp:82-88 [Conf]
  3. Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth
    The KDD Process for Extracting Useful Knowledge from Volumes of Data. [Citation Graph (2, 1)][DBLP]
    Commun. ACM, 1996, v:39, n:11, pp:27-34 [Journal]
  4. Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth
    Statistical Themes and Lessons for Data Mining. [Citation Graph (2, 0)][DBLP]
    Data Min. Knowl. Discov., 1997, v:1, n:1, pp:11-28 [Journal]
  5. Usama M. Fayyad, Padhraic Smyth, Nicholas Weir, S. George Djorgovski
    Automated Analysis and Exploration of Image Databases: Results, Progress, and Challenges. [Citation Graph (2, 0)][DBLP]
    J. Intell. Inf. Syst., 1995, v:4, n:1, pp:7-25 [Journal]
  6. Rodney M. Goodman, Padhraic Smyth
    Information-Theoretic Rule Induction. [Citation Graph (1, 0)][DBLP]
    ECAI, 1988, pp:357-362 [Conf]
  7. Padhraic Smyth, Rodney M. Goodman, Charles M. Higgins
    A Hybrid Rule-Based/Bayesian Classifier. [Citation Graph (1, 0)][DBLP]
    ECAI, 1990, pp:610-615 [Conf]
  8. Heikki Mannila, Padhraic Smyth
    Approximate Query Answering with Frequent Sets and Maximum Entropy. [Citation Graph (1, 0)][DBLP]
    ICDE, 2000, pp:309- [Conf]
  9. Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth
    From Data Mining to Knowledge Discovery in Databases. [Citation Graph (1, 0)][DBLP]
    AI Magazine, 1996, v:17, n:3, pp:37-54 [Journal]
  10. Gregory Piatetsky-Shapiro, Christopher J. Matheus, Padhraic Smyth, Ramasamy Uthurusamy
    KDD-93: Progress and Challenges in Knowledge Discovery in Databases. [Citation Graph (1, 0)][DBLP]
    AI Magazine, 1994, v:15, n:3, pp:77-82 [Journal]
  11. William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth
    Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. [Citation Graph (0, 0)][DBLP]
    AIME, 1997, pp:73-85 [Conf]
  12. Padhraic Smyth
    Data-Driven Discovery Using Probabilistic Hidden Variable Models. [Citation Graph (0, 0)][DBLP]
    ALT, 2006, pp:28- [Conf]
  13. Padhraic Smyth
    Data-Driven Discovery Using Probabilistic Hidden Variable Models. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2006, pp:13- [Conf]
  14. Usama M. Fayyad, Padhraic Smyth
    The Automated Analysis, Cataloging, and Searching of Digital Image Libraries: A Machine Learning Approach. [Citation Graph (0, 0)][DBLP]
    DL, 1994, pp:225-249 [Conf]
  15. Padhraic Smyth
    Breaking out of the Black-Box: Research Challenges in Data Mining. [Citation Graph (0, 0)][DBLP]
    DMKD, 2001, pp:- [Conf]
  16. Padhraic Smyth
    Learning with Mixture Models: Concepts and Applications. [Citation Graph (0, 0)][DBLP]
    ECML, 2002, pp:529-0 [Conf]
  17. Michael C. Burl, Usama M. Fayyad, Pietro Perona, Padhraic Smyth
    Automated Analysis of Radar Imagery of Venus: Handling Lack of Ground Truth. [Citation Graph (0, 0)][DBLP]
    ICIP (3), 1994, pp:236-240 [Conf]
  18. Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan
    Hierarchical Models for Screening of Iron Deficiency Anemia. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:77-86 [Conf]
  19. Rodney M. Goodman, Padhraic Smyth
    The Induction of Probabilistic Rule Sets - The Itrule Algorithm. [Citation Graph (0, 0)][DBLP]
    ML, 1989, pp:129-132 [Conf]
  20. Sergey Kirshner, Sridevi Parise, Padhraic Smyth
    Unsupervised Learning with Permuted Data. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:345-352 [Conf]
  21. Padhraic Smyth, Alexander Gray, Usama M. Fayyad
    Retrofitting Decision Tree Classifiers Using Kernel Density Estimation. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:506-514 [Conf]
  22. Padhraic Smyth, Jeff Mellstrom
    Detecting Novel Classes with Applications to Fault Diagnosis. [Citation Graph (0, 0)][DBLP]
    ML, 1992, pp:416-425 [Conf]
  23. Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath, Erick Cantú-Paz
    Probabilistic Model-Based Detection of Bent-Double Radio Galaxies. [Citation Graph (0, 0)][DBLP]
    ICPR (2), 2002, pp:499-502 [Conf]
  24. David Newman, Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers
    Analyzing Entities and Topics in News Articles Using Statistical Topic Models. [Citation Graph (0, 0)][DBLP]
    ISI, 2006, pp:93-104 [Conf]
  25. Xianping Ge, Padhraic Smyth
    Deformable Markov model templates for time-series pattern matching. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:81-90 [Conf]
  26. Igor V. Cadez, Scott Gaffney, Padhraic Smyth
    A general probabilistic framework for clustering individuals and objects. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:140-149 [Conf]
  27. Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White
    Visualization of navigation patterns on a Web site using model-based clustering. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:280-284 [Conf]
  28. Igor V. Cadez, Padhraic Smyth, Heikki Mannila
    Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. [Citation Graph (0, 0)][DBLP]
    KDD, 2001, pp:37-46 [Conf]
  29. Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, Padhraic Smyth
    Rule Discovery from Time Series. [Citation Graph (0, 0)][DBLP]
    KDD, 1998, pp:16-22 [Conf]
  30. Darya Chudova, Scott Gaffney, Eric Mjolsness, Padhraic Smyth
    Translation-invariant mixture models for curve clustering. [Citation Graph (0, 0)][DBLP]
    KDD, 2003, pp:79-88 [Conf]
  31. Darya Chudova, Padhraic Smyth
    Pattern discovery in sequences under a Markov assumption. [Citation Graph (0, 0)][DBLP]
    KDD, 2002, pp:153-162 [Conf]
  32. Scott Gaffney, Padhraic Smyth
    Trajectory Clustering with Mixtures of Regression Models. [Citation Graph (0, 0)][DBLP]
    KDD, 1999, pp:63-72 [Conf]
  33. Alexander T. Ihler, Jon Hutchins, Padhraic Smyth
    Adaptive event detection with time-varying poisson processes. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:207-216 [Conf]
  34. Eamonn J. Keogh, Padhraic Smyth
    A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:24-30 [Conf]
  35. Heikki Mannila, Dmitry Pavlov, Padhraic Smyth
    Prediction with Local Patterns using Cross-Entropy. [Citation Graph (0, 0)][DBLP]
    KDD, 1999, pp:357-361 [Conf]
  36. David Newman, Chaitanya Chemudugunta, Padhraic Smyth
    Statistical entity-topic models. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:680-686 [Conf]
  37. Dmitry Pavlov, Darya Chudova, Padhraic Smyth
    Towards scalable support vector machines using squashing. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:295-299 [Conf]
  38. Dmitry Pavlov, Padhraic Smyth
    Probabilistic query models for transaction data. [Citation Graph (0, 0)][DBLP]
    KDD, 2001, pp:164-173 [Conf]
  39. Padhraic Smyth
    Clustering Using Monte Carlo Cross-Validation. [Citation Graph (0, 0)][DBLP]
    KDD, 1996, pp:126-133 [Conf]
  40. Padhraic Smyth, Michael C. Burl, Usama M. Fayyad, Pietro Perona
    Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth. [Citation Graph (0, 0)][DBLP]
    KDD Workshop, 1994, pp:109-120 [Conf]
  41. Padhraic Smyth, Michael Ghil, Kayo Ide, Joseph Roden, Andrew Fraser
    Detecting Atmospheric Regimes Using Cross-Validated Clustering. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:61-66 [Conf]
  42. Padhraic Smyth, David Wolpert
    Anytime Exploratory Data Analysis for Massive Data Sets. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:54-60 [Conf]
  43. Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, Thomas L. Griffiths
    Probabilistic author-topic models for information discovery. [Citation Graph (0, 0)][DBLP]
    KDD, 2004, pp:306-315 [Conf]
  44. Scott White, Padhraic Smyth
    Algorithms for estimating relative importance in networks. [Citation Graph (0, 0)][DBLP]
    KDD, 2003, pp:266-275 [Conf]
  45. Seyoung Kim, Padhraic Smyth, Hal Stern
    A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data. [Citation Graph (0, 0)][DBLP]
    MICCAI (2), 2006, pp:217-224 [Conf]
  46. Seyoung Kim, Padhraic Smyth, Hal Stern, Jessica Turner
    Parametric Response Surface Models for Analysis of Multi-site fMRI Data. [Citation Graph (0, 0)][DBLP]
    MICCAI, 2005, pp:352-359 [Conf]
  47. Igor V. Cadez, Padhraic Smyth
    Model Complexity, Goodness of Fit and Diminishing Returns. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:388-394 [Conf]
  48. Igor V. Cadez, Padhraic Smyth
    Bayesian Predictive Profiles With Applications to Retail Transaction Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1353-1360 [Conf]
  49. Darya Chudova, Christopher Hart, Eric Mjolsness, Padhraic Smyth
    Gene Expression Clustering with Functional Mixture Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  50. Scott Gaffney, Padhraic Smyth
    Joint Probabilistic Curve Clustering and Alignment. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  51. Rodney M. Goodman, John W. Miller, Padhraic Smyth
    An Information Theoretic Approach to Rule-Based Connectionist Expert Systems. [Citation Graph (0, 0)][DBLP]
    NIPS, 1988, pp:256-263 [Conf]
  52. Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath
    Learning to Classify Galaxy Shapes Using the EM Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:1497-1504 [Conf]
  53. Padhraic Smyth
    On Stochastic Complexity and Admissible Models for Neural Network Classifiers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1990, pp:818-824 [Conf]
  54. Padhraic Smyth
    Probabilistic Anomaly Detection in Dynamic Systems. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:825-832 [Conf]
  55. Padhraic Smyth
    Clustering Sequences with Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:648-654 [Conf]
  56. Padhraic Smyth, Usama M. Fayyad, Michael C. Burl, Pietro Perona, Pierre Baldi
    Inferring Ground Truth from Subjective Labelling of Venus Images. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:1085-1092 [Conf]
  57. Padhraic Smyth, Jeff Mellstrom
    Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:667-674 [Conf]
  58. Padhraic Smyth, David Wolpert
    Stacked Density Estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  59. Padhraic Smyth
    Learning with Mixture Models: Concepts and Applications. [Citation Graph (0, 0)][DBLP]
    PKDD, 2002, pp:512- [Conf]
  60. Scott White, Padhraic Smyth
    A Spectral Clustering Approach To Finding Communities in Graph. [Citation Graph (0, 0)][DBLP]
    SDM, 2005, pp:- [Conf]
  61. Dmitry Pavlov, Padhraic Smyth
    Approximate Query Answering by Model Averaging. [Citation Graph (0, 0)][DBLP]
    SDM, 2003, pp:- [Conf]
  62. Xianping Ge, Wanda Pratt, Padhraic Smyth
    Discovering Chinese Words from Unsegmented Text (poster abstract). [Citation Graph (0, 0)][DBLP]
    SIGIR, 1999, pp:271-272 [Conf]
  63. Darya Chudova, Scott Gaffney, Padhraic Smyth
    Probabilistic Models For Joint Clustering And Time-Warping Of Multidimensional Curves. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:134-141 [Conf]
  64. Dmitry Pavlov, Heikki Mannila, Padhraic Smyth
    Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:465-472 [Conf]
  65. Chidanand Apté, Bing Liu, Edwin P. D. Pednault, Padhraic Smyth
    Business applications of data mining. [Citation Graph (0, 0)][DBLP]
    Commun. ACM, 2002, v:45, n:8, pp:49-53 [Journal]
  66. Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth
    Statistical Inference and Data Mining. [Citation Graph (0, 1)][DBLP]
    Commun. ACM, 1996, v:39, n:11, pp:35-41 [Journal]
  67. Padhraic Smyth, Daryl Pregibon, Christos Faloutsos
    Data-driven evolution of data mining algorithms. [Citation Graph (0, 0)][DBLP]
    Commun. ACM, 2002, v:45, n:8, pp:33-37 [Journal]
  68. Xianping Ge, David Eppstein, Padhraic Smyth
    The Distribution of Cycle Lengths in Graphical Models for Iterative Decoding [Citation Graph (0, 0)][DBLP]
    CoRR, 1999, v:0, n:, pp:- [Journal]
  69. Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White
    Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2003, v:7, n:4, pp:399-424 [Journal]
  70. Darya Chudova, Padhraic Smyth
    Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2003, v:7, n:3, pp:273-299 [Journal]
  71. Seyoung Kim, Padhraic Smyth
    Segmental Hidden Markov Models with Random Effects for Waveform Modeling. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:945-969 [Journal]
  72. Michael C. Burl, Lars Asker, Padhraic Smyth, Usama M. Fayyad, Pietro Perona, Larry Crumpler, Jayne Aubele
    Learning to Recognize Volcanoes on Venus. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1998, v:30, n:2-3, pp:165-194 [Journal]
  73. Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren
    Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:47, n:1, pp:7-34 [Journal]
  74. Pat Langley, Gregory M. Provan, Padhraic Smyth
    Learning with Probabilistic Representations. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:29, n:2-3, pp:91-101 [Journal]
  75. Padhraic Smyth, David Wolpert
    Linearly Combining Density Estimators via Stacking. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:36, n:1-2, pp:59-83 [Journal]
  76. Padhraic Smyth, David Heckerman, Michael I. Jordan
    Probabilistic Independence Networks for Hidden Markov Probability Models. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1997, v:9, n:2, pp:227-269 [Journal]
  77. Padhraic Smyth
    Hidden Markov models for fault detection in dynamic system. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1994, v:27, n:1, pp:149-164 [Journal]
  78. Padhraic Smyth
    Bounds on the mean classification error rate of multiple experts. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 1996, v:17, n:12, pp:1253-1257 [Journal]
  79. Padhraic Smyth
    Belief networks, hidden Markov models, and Markov random fields: A unifying view. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 1997, v:18, n:11-13, pp:1261-1268 [Journal]
  80. 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. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2000, v:2, n:2, pp:81-85 [Journal]
  81. Joshua O'Madadhain, Jon Hutchins, Padhraic Smyth
    Prediction and ranking algorithms for event-based network data. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2005, v:7, n:2, pp:23-30 [Journal]
  82. Xianping Ge, David Eppstein, Padhraic Smyth
    The distribution of loop lengths in graphical models for turbo decoding. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2001, v:47, n:6, pp:2549-2553 [Journal]
  83. Rodney M. Goodman, Padhraic Smyth
    Decision tree design from a communication theory standpoint. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1988, v:34, n:5, pp:979-994 [Journal]
  84. John W. Miller, Rodney M. Goodman, Padhraic Smyth
    On loss functions which minimize to conditional expected values and posterior proba- bilities. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1993, v:39, n:4, pp:1404-0 [Journal]
  85. Dmitry Pavlov, Heikki Mannila, Padhraic Smyth
    Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Knowl. Data Eng., 2003, v:15, n:6, pp:1409-1421 [Journal]
  86. Sergey Kirshner, Padhraic Smyth
    Infinite mixtures of trees. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:417-423 [Conf]
  87. David Newman, Kat Hagedorn, Chaitanya Chemudugunta, Padhraic Smyth
    Subject metadata enrichment using statistical topic models. [Citation Graph (0, 0)][DBLP]
    JCDL, 2007, pp:366-375 [Conf]
  88. Seyoung Kim, Padhraic Smyth
    Hierarchical Dirichlet Processes with Random Effects. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:697-704 [Conf]
  89. Alexander T. Ihler, Padhraic Smyth
    Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:625-632 [Conf]
  90. Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers
    Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:241-248 [Conf]
  91. Seyoung Kim, Padhraic Smyth, Stefan Luther
    Modeling Waveform Shapes with Random E ects Segmental Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    UAI, 2004, pp:309-316 [Conf]
  92. Sergey Kirshner, Padhraic Smyth, Andrew Robertson
    Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. [Citation Graph (0, 0)][DBLP]
    UAI, 2004, pp:317-314 [Conf]
  93. Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth
    The Author-Topic Model for Authors and Documents. [Citation Graph (0, 0)][DBLP]
    UAI, 2004, pp:487-494 [Conf]
  94. Ian R. Porteous, Alex Ihter, Padhraic Smyth, Max Welling
    Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. [Citation Graph (0, 0)][DBLP]
    UAI, 2006, pp:- [Conf]

  95. Combining concept hierarchies and statistical topic models. [Citation Graph (, )][DBLP]


  96. Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. [Citation Graph (, )][DBLP]


  97. Fast collapsed gibbs sampling for latent dirichlet allocation. [Citation Graph (, )][DBLP]


  98. Modeling relational events via latent classes. [Citation Graph (, )][DBLP]


  99. Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set. [Citation Graph (, )][DBLP]


  100. Distributed Inference for Latent Dirichlet Allocation. [Citation Graph (, )][DBLP]


  101. Asynchronous Distributed Learning of Topic Models. [Citation Graph (, )][DBLP]


  102. Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning. [Citation Graph (, )][DBLP]


  103. Bayesian detection of non-sinusoidal periodic patterns in circadian expression data. [Citation Graph (, )][DBLP]


  104. Estimating replicate time shifts using Gaussian process regression. [Citation Graph (, )][DBLP]


  105. Technical perspective - Creativity helps influence prediction precision. [Citation Graph (, )][DBLP]


  106. Text Modeling using Unsupervised Topic Models and Concept Hierarchies [Citation Graph (, )][DBLP]


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