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In this work, we address the problem of learning arm gestures from imitation by humanoid robots when the training set contains missing data. We assume that multiple gesture demonstrations are available. The problem is challenging because of the fact that there is no temporal alignment between the demonstrations. In this work, we propose two approaches to handle the missing data problem. One approach...
This paper presents an extension to the Rule-Based Similarity (RBS) model a novel rough set approach to the problem of learning a similarity relation from data. The original model, proposed in [1], applied the notion of Tversky's feature contrast model in a rough set framework to facilitate an accurate case-based classification. In the dynamic RBS model, a dynamic reducts technique is used to broaden...
Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works by aligning local linear models, based on a probabilistic formulation of subspace identification. Because the procedure for constructing a state sequence in subspace identification can be interpreted as the CCA between past and future...
This paper presents a platform to implement and evaluate a learning by imitation framework which enables humanoid robots to learn hand gestures from human beings. A marker based system is used to capture human motion data. From this data we extract the shoulder and elbow joint angles, which uniquely characterize a particular hand gesture. The proposed imitation learning framework aims to generalize...
In this paper a new tool is proposed as a possible aid to study differences and similarities between the human and the artificial neural network (NN) learning of some verbal and mathematical elementary abilities. For this purpose, simple NNs of the multi layer kind (MLNN) have been build. These MLNNs are able to recognize some graphemes and/or to make additions of integers up to 1000. An algorithm...
In this paper, we present fast algorithms on mining coevolving time series, with or with out missing values. Our algorithms could mine meaningful patterns effectively and efficiently. With those patterns, our algorithms can do forecasting, compression, and segmentation. Furthermore, we apply our algorithm to solve practical problems including occlusions in motion capture, and generating natural human...
Gait Energy Image (GEI) has been shown to be a robust gait descriptor for gait recognition, and many algorithms based on GEI have been proposed. We propose in this paper an improved algorithm to exploit the discriminative information of GEI in identifying walking people based on gait sequences. Specifically, we first obtain the discriminative power of each pixel in the GEI, referred to as feature...
This paper presents harmonious human-computer interaction research in pervasive environment based on dynamic sharing niche technology and co-evolutionary learning. It begins with a discussion of some important issues related to user-centered human-computer interaction. Then it describes niched co-evolving approach, and based on dynamic sharing niche technology and homophily principle, the group model...
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