Learning multiple concepts with incremental diverse density
We present a novel method of learning multiple disjunct concepts with diverse density using an incremental approach. We demonstrate that by maximizing the diverse density over individual target concept points and minimizing the probability of their intersection, concepts can be learned incrementally. This method reduces the complexity of the algorithm from factorial, with respect to the number of targets, to exponential order. We demonstrate that this greedy approach successfully learns disjunctive target concepts with competitive classification accuracy on a benchmark multiple instance learning dataset in comparison to other common diverse density approaches. We also introduce a novel application of the multiple instance learning framework to an emotion recognition task using prosodic and spectral speech features.