14. The computer readable media of claim 11 , wherein the computer readable media outputs the final classifier that is an ensemble of the selected weak learners corresponding to those views with the lowest training error at each iteration.
11. Computer readable media having computer readable instructions recorded thereon for fusing data from different information sources using a training set having a plurality of training examples, each training example having a plurality of disjoint views, said data from different information sources and said training examples being data from at least one sensor, the computer readable media comprising: an assignment module including instructions for initially assigning weights to each of a plurality of training examples; a training module including instructions for sampling the training examples based on the distribution of the weights of the training examples and, iteratively for each view, separately training weak classifiers in parallel on the sample of training examples; a selector including instructions for selecting a weak classifier at that iteration corresponding to the view with the lowest training error rate among all the views and calculating a combination weight value associated with the selected classifier as a function of the lowest training error rate at that iteration; an updater including instructions for assigning the weights for all views of that training example with the same updated weight for sampling the training example, the updated weight distribution being a function of the lowest training error rate among all views at that iteration; wherein the training module, the selector, and the updater repeat said sampling, said training, said selecting, and said updating for a predetermined number of iterations; and instructions for forming a final classifier that is a sum of the selected weak classifiers weighted by the corresponding combination weight value at each iteration.