Adaboost.RT is a well-known extension of Adaboost to regression problems, which achieves increased accuracy by iterative training of weak learners on different subsets of data. At each iteration, the prediction error is compared against a threshold, which is used to increase or decrease the weight of the sample for the next iteration. Adaboost.RT is susceptible to noise and contains a singularity in its misclassification function, which results in reduced accuracy for output values near zero. We propose Adaboost.MRT, which extends Adaboost.RT to multivariate output, addresses the singularity in the misclassification function and reduces noise sensitivity. A singularity-free, variance-scaled misclassification function is proposed that generates diversity in the training sets. Adaboost.MRT boosts multivariate regression by assigning each output variable a weight for each sample in the training data. To avoid fitting to outliers, the sampling weights for the training sets are averaged across all output variables. The threshold parameter is extended to accommodate the multivariate output and experiments suggest that for small amounts of output variables, the threshold can be tuned for each output variable individually. Comparisons on six singlevariate output datasets show that the proposed Adaboost.MRT outperforms Adaboost.RT on datasets with values near zero or with large noise and displays a similar accuracy otherwise. Experiments with three multivariate output datasets show that Adaboost.MRT performs similar or better than bagging and a simple averaging ensemble.
The Empathy Machine is an interactive installation that augments a visitor's empathic sense during a social conversation. Empathy is a key component of interpersonal interactions that is often neglected by modern communication technologies. This system uses facial expression recognition to identify the emotional state of a user's conversation partner. It algorithmically generates emotional music to match the expressive state of the partner and plays the music to the user in a non-disruptive manner. The result is an augmentation of the user's emotional response to the emotional expression of their partner.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.