The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An optimal algorithm for approximate nearest neighbor searching
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Alpha seeding for support vector machines
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Proceedings of the 13th annual ACM international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Interactive objects retrieval with efficient boosting
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Incremental query evaluation for support vector machines
Proceedings of the 18th ACM conference on Information and knowledge management
Relevance feature mapping for content-based image retrieval
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Exact indexing for support vector machines
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
SALSAS: Sub-linear active learning strategy with approximate k-NN search
Pattern Recognition
Relevance feature mapping for content-based multimedia information retrieval
Pattern Recognition
Semi-supervised image classification for automatic construction of a health image library
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
iKernel: Exact indexing for support vector machines
Information Sciences: an International Journal
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A query can be answered by a binary classifier, which separates the instances that are relevant to the query from the ones that are not. When kernel methods are employed to train such a classifier, the class boundary is represented as a hyperplane in a projected space. Data instances that are farthest from the hyperplane are deemed to be most relevant to the query, and that are nearest to the hyperplane to be most uncertain to the query. In this paper, we address the twin problems of efficient retrieval of the approximate set of instances (a) farthest from and (b) nearest to a query hyperplane. Retrieval of instances for this hyperplane-based query scenario is mapped to the range-query problem allowing for the reuse of existing index structures. Empirical evaluation on large image datasets confirms the effectiveness of our approach.