We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens

A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint

A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L

Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies to datasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning

We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed processing requirements of a large scale simulation. The volume of the data is such that classifiers can train only on data local to a given partition. As a result of the partition reflecting the needs of the simulation, the class statistics can vary from partition to partition. Some classes will likely be missing from some partitions. We combine a fast ensemble learning algorithm with probabilistic majority voting in order to learn an accurate classifier from such data. Results from simulations of an impactor bar crushing a storage canister and from facial feature recognition show that regions of interest are successfully identified in spite of the class imbalance in the individual training sets. Ó 2007 Elsevier B.V. All rights reserved. Keywords: Random forest; Saliency; Probabilistic voting; Out-of-partition; k-Nearest centroids; Imbalanced training data

In this paper, we present boundary labeling, a new approach for labeling point sets with large labels. We first place disjoint labels around an axis-parallel rectangle that contains the points. Then we connect each label to its point such that no two connections intersect. Such an approach

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12 September 2007 Disk Used 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process-12 ing requirements of a large scale simulation. The volume of the data is such that classifiers can train only on data local to a given par-13 tition. As a result of the partition reflecting the needs of the simulation, the class statistics can vary from partition to partition. Some 14 classes will likely be missing from some partitions. We combine a fast ensemble learning algorithm with probabilistic majority voting 15 in order to learn an accurate classifier from such data. Results from simulations of an impactor bar crushing a storage canister and from 16 facial feature recognition show that regions of interest are successfully identified in spite of the class imbalance in the individual training 17 sets.

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