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Segmentation, Memorization, recognition and Abstraction of Humanoid Motions Based on Correlations and Associative Memory

Segmentation, Memorization, Recognition and Abstraction of H...

引用

IEEE-RAS international conference on Humanoid Robots

作者： Hideki Kadone Yoshihiko Nakamura Department of Mechano-Informatics Graduate School of Information Science and Technology University of Tokyo Tokyo Japan

ISBN: (纸本)1424401992;142440200X

In order to self-organize symbols from observed motion patterns, it is necessary to temporally segment the continuous motion pattern flows into meaningful chunks. For reusability of the acquired information, repeatedly observed patterns are important, which means that segmentation, memorization, recognition and abstraction depend on each other. From this point of view, we propose methods for motion patterns of humanoid robots observed as a continuous flow using pattern correlations and associative memory. Initially, patterns are segmented by pattern correlations and then stored into the associative memory. Afterwards, only new kinds of motions are fed through this process. Associative memory is capable of segmentation, recognition and abstraction, and has ease in incremental update of the stroge for new patterns

关键词： Associative memory pattern recognition Humanoid robots Information science Intelligent systems data mining Frequency Motion control pattern matching Neural networks

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GCA: A real-time grid-based clustering algorithm for large data set

GCA: A real-time grid-based clustering algorithm for large d...

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international conference on pattern recognition

作者： Zhiwen Yu Hau-San Wong Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (纸本)0769525210

Few of the current existing methods for unsupervised learning (clustering) algorithms consider clustering the data points in a low-dimensional subspace in real time. In this paper, we present a grid based clustering algorithm (GCA) with time complexity (O(n)). Unlike previous clustering algorithm, GCA pays more attention to the running time of the algorithm. GCA achieves low running time by (i) determining the number of the clusters according to the point density of the grid cell and (ii) computing the distances between the centers of the clusters and the grid cells, not the data points. In order to make GCA more efficient, principal component analysis (PCA) is introduced to transform the data points from high dimension to low dimension. Finally, we analyze the performance of GCA and show that it outperforms most of the current state-of-the-art methods in terms of efficiency. In particular, it outperforms k-means algorithm by several orders in the running time

关键词： Clustering algorithms Computer science Grid computing Unsupervised learning Performance analysis machine intelligence pattern recognition data mining databases Kernel

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An Improved Semi-Supervised Support Vector machine Based Translation Algorithm for BCI Systems

An Improved Semi-Supervised Support Vector Machine Based Tra...

引用

international conference on pattern recognition

作者： Jianzhao Qin Yuanqing Li Institute of Automation Science and Engineering South China University of Technology Guangzhou China Institute for Infocomm Research Singapore

ISBN: (纸本)0769525210

In this study, we propose an improved semi-supervised support vector machine (SVM) based translation algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process and enhancing the adaptability of BCI systems. In this algorithm, we apply a semi-supervised SVM, which builds a SVM classifier based on small amounts of labeled data and large amounts of unlabeled data, to translating the features extracted from the electrical recordings of brain into control signals. For reducing the time to train the semi-supervised SVM, we improve it by introducing a batch-mode incremental training method, which also can be used to enhance the adaptability of online BCI systems. the off-line data analysis results demonstrated the effectiveness of our algorithm

关键词： Support vector machines Support vector machine classification Linear programming Feature extraction Semisupervised learning Electroencephalography Brain computer interfaces Automation data mining data analysis

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Clustering support vector machines and its application to local protein tertiary structure prediction

Clustering support vector machines and its application to lo...

引用

6th international conference on Computational Science (ICCS 2006)

作者： He, Jieyue Zhong, Wei Harrison, Robert Tai, Phang C. Pan, Yi SE Univ Dept Comp Sci Nanjing 210096 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Georgia State Univ Dept Biol Atlanta GA 30303 USA

ISBN: (纸本)3540343814

Support Vector machines (SVMs) are new generation of machine learning techniques and have shown strong generalization capability for many data mining tasks. SVMs can handle nonlinear classification by implicitly mapping input samples from the input feature space into another high dimensional feature space with a nonlinear kernel function. However, SVMs are not favorable for huge datasets with over millions of samples. Granular computing decomposes information in the form of some aggregates and solves the targeted problems in each granule. therefore, we propose a novel computational model called Clustering Support Vector machines (CSVMs) to deal with the complex classification problems for huge datasets. Taking advantage of both theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. this feature makes learning tasks for each CSVMs more specific and simpler. Moreover, CSVMs built particularly for each granule can be easily parallelized so that CSVMs can be used to handle huge datasets efficiently. the CSVMs model is used for predicting local protein tertiary structure. Compared with the conventional clustering method, the prediction accuracy for local protein tertiary structure has been improved noticeably when the new CSVM model is used. the encouraging experimental results indicate that our new computational model opens a new way to solve the complex classification for huge datasets.

关键词： learning systems

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Conversation Robot with the Function of Gaze recognition

Conversation Robot with the Function of Gaze Recognition

引用

IEEE-RAS international conference on Humanoid Robots

作者： Shinya Fujie Toshihiko Yamahata Tetsunori Kobayashi Advanced Research Institute for Science and Engineering Waseda University Tokyo Japan School of Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)1424401992

Gaze recognition for conversation robot is realized and its effectiveness is confirmed. In human conversation, in addition to speech information, visual information plays important role. Particularly, gaze direction is a very useful prompt for turn-taking. In the case that the speaker finish his utterance, for example, if he looks at the listener, then he expect the listener to speak. On the other hand, if the speaker does not look at the listener, he tries to keep his turn. Most conventional spoken dialogue systems detect the finish of user's turn only by speech recognition. these systems cannot understand the user tries to keep his turn, and they wrongly begin the utterance and block the user's remaining utterance. In this study, we implement the gaze recognition using the user's image captured by the camera mounted on the eye of the robot and apply the recognition results to decide who should speak next. For gaze recognition, we introduce the sub-image of user's eye region extracted with the active appearance model as the feature. recognition with subspace method using this feature achieved 70% in recognition rate. Finally, the effectiveness of the gaze recognition is confirmed through the subjective experiment. the experiment is performed by the actual conversation between the conversation robot and the subject.

关键词： Robots Speech recognition Image recognition Target recognition Humans Image converters data mining Iris pattern recognition Control systems

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Humanoid Robot's Autonomous Acquisition of Proto-Symbols through Motion Segmentation

Humanoid Robot's Autonomous Acquisition of Proto-Symbols thr...

引用

IEEE-RAS international conference on Humanoid Robots

作者： Wataru Takano Yoshihiko Nakamura Department of Mechano-Informatics University of Tokyo Bunkyo-ku Tokyo Japan

ISBN: (纸本)1424401992

Mimesis is the theory that human intelligence originated in the interactive communication of motion recognition and generation through imitation. A mimesis model has been proposed using hidden Markov models (HMMs), which represent proto symbols. In our previous system, the user had to manually divided a sequence of motion into segments in order to embed each segment as an HMM. Automatic segmentation is essential for a system to autonomously learn and develop through imitation. In this paper, we propose an automatic motion segmentation method utilizing correlation among movements for a short time period. In addition, we show that it is possible to acquire proto symbols by providing the automatically segmented motion patterns with the mimesis system

关键词： Humanoid robots Motion segmentation Computer vision Hidden Markov models Humans Neurons data mining learning Motion control Mirrors

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Robot learning of upper-body human motion by active imitation

Robot learning of upper-body human motion by active imitatio...

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IEEE-RAS international conference on Humanoid Robots

作者： J.P. Bandera R. Marfil L. Molina-Tanco J.A. Rodriguez A. Bandera F. Sandoval Grupo de Ingeniería y Sistemas Integrados Department Tecnología Electrónica Universidad de Málaga Malaga Spain

ISBN: (纸本)1424401992;142440200X

this paper presents a general architecture that allows a humanoid robot to imitate upper-body movements of a human demonstrator. this architecture integrates a mechanism to memorize novel behaviours executed by a human demonstrator, with a module to recognize and generate its own interpretation of already observed behaviours. Our imitator includes three biologically plausible components: i) an attention mechanism to autonomously extract relevant information from the visual input; ii) a supra-modal representation of the motion of observed body parts to map visual and motor domains; and iii) an active imitation module which involves the motor systems in the behaviour recognition process. Experimental results with a real humanoid robot demonstrate the ability of the proposed architecture to acquire novel behaviours and to recognize and reproduce previously memorized ones

关键词： Humans Cognitive robotics Humanoid robots data mining Testing Animals Dolphins Cultural differences Research and development Brain modeling

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Understanding patterns with different subspace classification

引用

4th international conference on machine learning and data mining in pattern recognition

作者： Szepannek, G Luebke, K Weihs, C Univ Dortmund Dept Stat Dortmund Germany

ISBN: (纸本)3540269231

By identifying characteristic regions in which classes are dense and also relevant for discrimination a new, intuitive classification method is set up. this method enables a visualized result so the user is provided with an insight into the data with respect to discrimination for an easy interpretation. Additionally, it outperforms Decision trees in a lot of situations and is robust against outliers and missing values.

关键词： pattern recognition

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Disjunctive sequential patterns on single data sequence and its anti-monotonicity

引用

4th international conference on machine learning and data mining in pattern recognition

作者： Shimizu, K Miura, T HOSEI Univ Dept Elect & Elect Engn Koganei Tokyo 1848584 Japan

In this work, we proposes a novel method for mining frequent disjunctive patterns on single data sequence. For this purpose, we introduce a sophisticated measure that satisfies anti-monotonicity, by which we can discu... 详细信息

ISBN: (纸本)3540269231

关键词： disjunctive sequence pattern anti-monotonicity single data sequence

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Prediction of secondary protein structure content from primary sequence alone - A feature selection based approach

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4th international conference on machine learning and data mining in pattern recognition

作者： Kurgan, L Homaeian, L Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

ISBN: (纸本)3540269231

Research in protein structure and function is one of the most important subjects in modem bioinformatics and computational biology. It often uses advanced data mining and machine learning methodologies to perform prediction or pattern recognition tasks. this paper describes a new method for prediction of protein secondary structure content based on feature selection and multiple linear regression. the method develops a novel representation of primary protein sequences based on a large set of 495 features. the feature selection task performed using very large set of nearly 6,000 proteins, and tests performed on standard non-homologues protein sets confirm high quality of the developed solution. the application of feature selection and the novel representation resulted in 14-15% error rate reduction when compared to results achieved when standard representation is used. the prediction tests also show that a small set of 5-25 features is sufficient to achieve accurate prediction for both helix and strand content for non-homologous proteins.

关键词： Proteins

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