ICML 2006 - PDF Preprint Papers

ICML 2006 Workshop on Statistical Network Analysis

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ICML
2006
IEEE

522views Machine Learning» more ICML 2006»

Cover Trees for Nearest Neighbor

15 years 9 days ago

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We present a tree data structure for fast nearest neighbor operations in general n- point metric spaces (where the data set con- sists of n points). The data structure re- quir...

Alina Beygelzimer, Sham Kakade, John Langford

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Id	Views	Title	Status

1	522	Cover Trees for Nearest Neighbor
2	405	The support vector decomposition machine	claim
3	279	Pachinko allocation: DAG-structured mixture models of topic correlations	claim
4	256	Automatic basis function construction for approximate dynamic programming and reinforcement learning	claim
5	248	R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization	claim
6	219	Null space versus orthogonal linear discriminant analysis	claim
7	211	Cost-sensitive learning with conditional Markov networks	claim
8	205	Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks	claim
9	201	Batch mode active learning and its application to medical image classification	claim
10	197	Learning high-order MRF priors of color images	claim
11	194	Convex optimization techniques for fitting sparse Gaussian graphical models	claim
12	193	Maximum margin planning	claim
13	188	Totally corrective boosting algorithms that maximize the margin	claim
14	188	Active learning via transductive experimental design	claim
15	187	Dynamic topic models	claim
16	186	Discriminative unsupervised learning of structured predictors	claim
17	185	A DC-programming algorithm for kernel selection	claim
18	182	Locally adaptive classification piloted by uncertainty	claim
19	182	Learning a kernel function for classification with small training samples	claim
20	180	Topic modeling: beyond bag-of-words	claim
21	180	Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems	claim
22	176	Reinforcement learning for optimized trade execution	claim
23	174	Combined central and subspace clustering for computer vision applications	claim
24	174	MISSL: multiple-instance semi-supervised learning	claim
25	172	Concept boundary detection for speeding up SVMs	claim
26	171	An empirical comparison of supervised learning algorithms	claim
27	170	Active sampling for detecting irrelevant features	claim
28	170	Collaborative ordinal regression	claim
29	167	On a theory of learning with similarity functions	claim
30	165	Learning low-rank kernel matrices	claim
31	164	Kernelizing the output of tree-based methods	claim
32	162	Efficient lazy elimination for averaged one-dependence estimators	claim
33	161	Bayesian learning of measurement and structural models	claim
34	161	Bayesian regression with input noise for high dimensional data	claim
35	160	Fast and space efficient string kernels using suffix arrays	claim
36	159	Block-quantized kernel matrix for fast spectral embedding	claim
37	158	Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution	claim
38	158	Practical solutions to the problem of diagonal dominance in kernel document clustering	claim
39	157	Multiclass reduced-set support vector machines	claim
40	156	Learning the structure of Factored Markov Decision Processes in reinforcement learning problems	claim
41	156	Deterministic annealing for semi-supervised kernel machines	claim
42	154	Ranking on graph data	claim
43	154	On Bayesian bounds	claim
44	154	Nightmare at test time: robust learning by feature deletion	claim
45	154	Local distance preservation in the GP-LVM through back constraints	claim
46	153	Autonomous shaping: knowledge transfer in reinforcement learning	claim
47	152	Nonstationary kernel combination	claim
48	152	Online decoding of Markov models under latency constraints	claim
49	151	Efficient MAP approximation for dense energy functions	claim
50	151	Accelerated training of conditional random fields with stochastic gradient methods	claim
51	151	Learning user preferences for sets of objects	claim
52	150	A new approach to data driven clustering	claim
53	148	Bayesian pattern ranking for move prediction in the game of Go	claim
54	147	A choice model with infinitely many latent features	claim
55	147	A duality view of spectral methods for dimensionality reduction	claim
56	146	Combining discriminative features to infer complex trajectories	claim
57	145	Full Bayesian network classifiers	claim
58	144	Quadratic programming relaxations for metric labeling and Markov random field MAP estimation	claim
59	144	Probabilistic inference for solving discrete and continuous state Markov Decision Processes	claim
60	143	Higher order learning with graphs	claim
61	143	Algorithms for portfolio management based on the Newton method	claim
62	143	Fast direct policy evaluation using multiscale analysis of Markov diffusion processes	claim
63	143	Efficient inference on sequence segmentation models	claim
64	143	Feature subset selection bias for classification learning	claim
65	143	Semi-supervised nonlinear dimensionality reduction	claim
66	142	An intrinsic reward mechanism for efficient exploration	claim
67	140	Learning hierarchical task networks by observation	claim
68	137	Predictive linear-Gaussian models of controlled stochastic dynamical systems	claim
69	137	Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture	claim
70	136	Regression with the optimised combination technique	claim
71	136	An analytic solution to discrete Bayesian reinforcement learning	claim
72	134	Statistical debugging: simultaneous identification of multiple bugs	claim
73	131	Relational temporal difference learning	claim
74	131	An analysis of graph cut size for transductive learning	claim
75	131	Hidden process models	claim
76	131	Generalized spectral bounds for sparse LDA	claim
77	131	How boosting the margin can also boost classifier complexity	claim
78	131	PAC model-free reinforcement learning	claim
79	130	Agnostic active learning	claim
80	130	The rate adapting poisson model for information retrieval and object recognition	claim
81	128	Online multiclass learning by interclass hypothesis sharing	claim
82	128	Discriminative cluster analysis	claim
83	127	Local Fisher discriminant analysis for supervised dimensionality reduction	claim
84	126	Personalized handwriting recognition via biased regularization	claim
85	126	Kernel information embeddings	claim
86	126	Predictive search distributions	claim
87	125	Iterative RELIEF for feature weighting	claim
88	124	Simpler knowledge-based support vector machines	claim
89	124	Clustering graphs by weighted substructure mining	claim
90	124	Fast time series classification using numerosity reduction	claim
91	123	Two-dimensional solution path for support vector regression	claim
92	122	The relationship between Precision-Recall and ROC curves	claim
93	121	Data association for topic intensity tracking	claim
94	120	Spectral clustering for multi-type relational data	claim
95	119	Learning predictive state representations using non-blind policies	claim
96	119	Graph model selection using maximum likelihood	claim
97	119	Pruning in ordered bagging ensembles	claim
98	118	Constructing informative priors using transfer learning	claim
99	117	Hierarchical classification: combining Bayes with SVM	claim
100	117	A continuation method for semi-supervised SVMs	claim
101	117	Estimating relatedness via data compression	claim
102	116	Efficient co-regularised least squares regression	claim
103	114	Region-based value iteration for partially observable Markov decision processes	claim
104	113	Trading convexity for scalability	claim
105	113	A graphical model for predicting protein molecular function	claim
106	113	Categorization in multiple category systems	claim
107	112	Fast particle smoothing: if I had a million particles	claim
108	111	A regularization framework for multiple-instance learning	claim
109	111	Sequential update of ADtrees	claim
110	111	Feature value acquisition in testing: a sequential batch test algorithm	claim
111	110	Learning to impersonate	claim
112	109	Using query-specific variance estimates to combine Bayesian classifiers	claim
113	108	Robust Euclidean embedding	claim
114	108	Optimal kernel selection in Kernel Fisher discriminant analysis	claim
115	108	Experience-efficient learning in associative bandit problems	claim
116	105	Predictive state representations with options	claim
117	104	Efficient learning of Naive Bayes classifiers under class-conditional classification noise	claim
118	103	Using inaccurate models in reinforcement learning	claim
119	101	Qualitative reinforcement learning	claim
120	101	Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features	claim
121	100	Semi-supervised learning for structured output variables	claim
122	99	Ranking individuals by group comparisons	claim
123	99	Permutation invariant SVMs	claim
124	97	Robust probabilistic projections	claim
125	97	Pareto optimal linear classification	claim
126	97	A statistical approach to rule learning	claim
127	95	Inference with the Universum	claim
128	94	Multiclass boosting with repartitioning	claim
129	90	Learning algorithms for online principal-agent problems (and selling goods online)	claim
130	86	An investigation of computational and informational limits in Gaussian mixture clustering	claim
131	85	Fast nonparametric clustering with Gaussian blurring mean-shift	claim

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