See Your Emotion from Gait Using Unlabeled Skeleton Data

Using Speech Data to Recognize Emotion in Human Gait

Robots that can recognize emotions can improve humans’ mental health by providing empathy and social communication. Emotion recognition by robots is challenging because unlike in human-computer environments, facial information is not always available. Instead, our method proposes using speech and gait analysis to recognize human emotion. Previous research suggests that the dynamics of emotional...

Face Recognition Using Unlabeled Data

Face recognition systems can normally attain good accuracy when they are provided with a large set of training examples. However, when a large training set is not available, their performance is commonly poor. In this work we describe a method for face recognition that achieves good results when only a very small training set is available (it can work with a training set as small as one image p...

Estimating Accuracy from Unlabeled Data

We consider the question of how unlabeled data can be used to estimate the true accuracy of learned classifiers. This is an important question for any autonomous learning system that must estimate its accuracy without supervision, and also when classifiers trained from one data distribution must be applied to a new distribution (e.g., document classifiers trained on one text corpus are to be ap...

Learning from Labeled and Unlabeled Data using Graph Min uts

Using Weighted Nearest Neighbor to Benefit from Unlabeled Data

The development of data-mining applications such as textclassification and molecular profiling has shown the need for machine learning algorithms that can benefit from both labeled and unlabeled data, where often the unlabeled examples greatly outnumber the labeled examples. In this paper we present a two-stage classifier that improves its predictive accuracy by making use of the available unla...