1. A computer-implemented robust method 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, the method comprising: a) initially assigning weights, by the computer, to each of the plurality of training examples; b) sampling the training examples, by the computer, based on the distribution of the weights of the training examples; c) iteratively, by the computer, for each of the views, separately training weak classifiers in parallel on the sample of training examples; d) selecting, by the computer, the weak classifier 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; e) updating the weights of the sampled training examples by, for each of the sampled training examples, assigning the same updated weight for sampling the training examples for all views, the updated weight distribution being a function of-the lowest training error rate among all views at that iteration; f) repeating said b) sampling, c) training, d) selecting, and e) updating for a predetermined number of iterations; and g) forming a final classifier that is a sum of the selected weak learners weighted by the corresponding combination weight value at each iteration.
1. A computer-implemented robust method 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, the method comprising: a) initially assigning weights, by the computer, to each of the plurality of training examples; b) sampling the training examples, by the computer, based on the distribution of the weights of the training examples; c) iteratively, by the computer, for each of the views, separately training weak classifiers in parallel on the sample of training examples; d) selecting, by the computer, the weak classifier 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; e) updating the weights of the sampled training examples by, for each of the sampled training examples, assigning the same updated weight for sampling the training examples for all views, the updated weight distribution being a function of-the lowest training error rate among all views at that iteration; f) repeating said b) sampling, c) training, d) selecting, and e) updating for a predetermined number of iterations; and g) forming a final classifier that is a sum of the selected weak learners weighted by the corresponding combination weight value at each iteration. 10. The computer-implemented method of claim 1 , wherein the disjoint views are from a same data set, each view capturing a different aspect of the data.