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ICML 2005 Workshop on Statistical Network Analysis

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ICML
2005
IEEE

279views Machine Learning» more ICML 2005»

Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes

14 years 5 months ago

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The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning me...

Yushi Jing, Vladimir Pavlovic, James M. Rehg

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Id	Views	Title	Status

1	279	Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes	claim
2	226	Discriminative versus generative parameter and structure learning of Bayesian network classifiers	claim
3	215	Augmenting naive Bayes for ranking	claim
4	210	Linear Asymmetric Classifier for cascade detectors	claim
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8	196	Bayesian sparse sampling for on-line reward optimization	claim
9	195	Active learning for Hidden Markov Models: objective functions and algorithms	claim
10	194	Q-learning of sequential attention for visual object recognition from informative local descriptors	claim
11	188	High speed obstacle avoidance using monocular vision and reinforcement learning	claim
12	187	Learning from labeled and unlabeled data on a directed graph	claim
13	186	A smoothed boosting algorithm using probabilistic output codes	claim
14	175	Learning class-discriminative dynamic Bayesian networks	claim
15	173	Predicting protein folds with structural repeats using a chain graph model	claim
16	169	Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields	claim
17	168	Experimental comparison between bagging and Monte Carlo ensemble classification	claim
18	168	A comparison of tight generalization error bounds	claim
19	167	Analysis and extension of spectral methods for nonlinear dimensionality reduction	claim
20	166	Generalized LARS as an effective feature selection tool for text classification with SVMs	claim
21	166	Incomplete-data classification using logistic regression	claim
22	165	Supervised dimensionality reduction using mixture models	claim
23	162	Clustering through ranking on manifolds	claim
24	161	Adapting two-class support vector classification methods to many class problems	claim
25	160	Building Sparse Large Margin Classifiers	claim
26	159	Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees	claim
27	159	Non-negative tensor factorization with applications to statistics and computer vision	claim
28	157	A causal approach to hierarchical decomposition of factored MDPs	claim
29	153	Using additive expert ensembles to cope with concept drift	claim
30	151	Implicit surface modelling as an eigenvalue problem	claim
31	150	Coarticulation: an approach for generating concurrent plans in Markov decision processes	claim
32	147	Learning Gaussian processes from multiple tasks	claim
33	145	Bayesian hierarchical clustering	claim
34	145	Proto-value functions: developmental reinforcement learning	claim
35	144	Healing the relevance vector machine through augmentation	claim
36	143	Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM	claim
37	143	Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning	claim
38	142	Dirichlet enhanced relational learning	claim
39	141	Tempering for Bayesian C&RT	claim
40	141	Active learning for sampling in time-series experiments with application to gene expression analysis	claim
41	141	Compact approximations to Bayesian predictive distributions	claim
42	140	The cross entropy method for classification	claim
43	139	ROC confidence bands: an empirical evaluation	claim
44	138	Error bounds for correlation clustering	claim
45	138	Semi-supervised graph clustering: a kernel approach	claim
46	137	Learning to compete, compromise, and cooperate in repeated general-sum games	claim
47	137	A practical generalization of Fourier-based learning	claim
48	137	Large margin non-linear embedding	claim
49	136	Learning as search optimization: approximate large margin methods for structured prediction	claim
50	135	Supervised clustering with support vector machines	claim
51	135	Finite time bounds for sampling based fitted value iteration	claim
52	134	Computational aspects of Bayesian partition models	claim
53	133	Preference learning with Gaussian processes	claim
54	133	A general regression technique for learning transductions	claim
55	133	Statistical and computational analysis of locality preserving projection	claim
56	133	Learn to weight terms in information retrieval using category information	claim
57	133	Naive Bayes models for probability estimation	claim
58	133	Comparing clusterings: an axiomatic view	claim
59	133	A theoretical analysis of Model-Based Interval Estimation	claim
60	132	A martingale framework for concept change detection in time-varying data streams	claim
61	130	Core Vector Regression for very large regression problems	claim
62	129	Large scale genomic sequence SVM classifiers	claim
63	128	An efficient method for simplifying support vector machines	claim
64	127	Exploration and apprenticeship learning in reinforcement learning	claim
65	127	Online learning over graphs	claim
66	126	Learning hierarchical multi-category text classification models	claim
67	126	Beyond the point cloud: from transductive to semi-supervised learning	claim
68	126	Learning structured prediction models: a large margin approach	claim
69	126	Hierarchical Dirichlet model for document classification	claim
70	125	New approaches to support vector ordinal regression	claim
71	125	Optimal assignment kernels for attributed molecular graphs	claim
72	125	Heteroscedastic Gaussian process regression	claim
73	125	Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning	claim
74	124	A brain computer interface with online feedback based on magnetoencephalography	claim
75	124	Supervised versus multiple instance learning: an empirical comparison	claim
76	123	A model for handling approximate, noisy or incomplete labeling in text classification	claim
77	121	Combining model-based and instance-based learning for first order regression	claim
78	121	Integer linear programming inference for conditional random fields	claim
79	120	Evaluating machine learning for information extraction	claim
80	119	Dynamic preferences in multi-criteria reinforcement learning	claim
81	119	Multimodal oriented discriminant analysis	claim
82	118	Why skewing works: learning difficult Boolean functions with greedy tree learners	claim
83	117	Hierarchic Bayesian models for kernel learning	claim
84	117	Robust one-class clustering using hybrid global and local search	claim
85	117	Unsupervised evidence integration	claim
86	117	New kernels for protein structural motif discovery and function classification	claim
87	116	Predicting probability distributions for surf height using an ensemble of mixture density networks	claim
88	114	Independent subspace analysis using geodesic spanning trees	claim
89	113	Learning discontinuities with products-of-sigmoids for switching between local models	claim
90	112	Multi-instance tree learning	claim
91	112	Learning first-order probabilistic models with combining rules	claim
92	112	Expectation maximization algorithms for conditional likelihoods	claim
93	111	Action respecting embedding	claim
94	110	Modeling word burstiness using the Dirichlet distribution	claim
95	108	Learning strategies for story comprehension: a reinforcement learning approach	claim
96	105	Ensembles of biased classifiers	claim
97	105	Generalized skewing for functions with continuous and nominal attributes	claim
98	105	Fast maximum margin matrix factorization for collaborative prediction	claim
99	104	Fast condensed nearest neighbor rule	claim
100	104	New d-separation identification results for learning continuous latent variable models	claim
101	103	A support vector method for multivariate performance measures	claim
102	103	Recycling data for multi-agent learning	claim
103	102	Variational Bayesian image modelling	claim
104	102	Near-optimal sensor placements in Gaussian processes	claim
105	102	Learning the structure of Markov logic networks	claim
106	102	Weighted decomposition kernels	claim
107	101	Reducing overfitting in process model induction	claim
108	101	Logistic regression with an auxiliary data source	claim
109	100	Reinforcement learning with Gaussian processes	claim
110	100	A graphical model for chord progressions embedded in a psychoacoustic space	claim
111	99	Predicting good probabilities with supervised learning	claim
112	99	Object correspondence as a machine learning problem	claim
113	99	Identifying useful subgoals in reinforcement learning by local graph partitioning	claim
114	98	Intrinsic dimensionality estimation of submanifolds in Rd	claim
115	98	Multi-class protein fold recognition using adaptive codes	claim
116	97	Learning to rank using gradient descent	claim
117	96	Closed-form dual perturb and combine for tree-based models	claim
118	94	Multi-way distributional clustering via pairwise interactions	claim
119	94	Predicting relative performance of classifiers from samples	claim
120	93	Relating reinforcement learning performance to classification performance	claim
121	92	Learning approximate preconditions for methods in hierarchical plans	claim
122	91	Fast inference and learning in large-state-space HMMs	claim
123	90	Propagating distributions on a hypergraph by dual information regularization	claim
124	89	Online feature selection for pixel classification	claim
125	88	Error limiting reductions between classification tasks	claim
126	88	Learning predictive representations from a history	claim
127	87	PAC-Bayes risk bounds for sample-compressed Gibbs classifiers	claim
128	85	Hedged learning: regret-minimization with learning experts	claim
129	84	Optimizing abstaining classifiers using ROC analysis	claim
130	79	Predictive low-rank decomposition for kernel methods	claim
131	73	Unifying the error-correcting and output-code AdaBoost within the margin framework	claim

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