The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Yoji Uno - One of the best experts on this subject based on the ideXlab platform.
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a forward Propagation learning Rule for neural inverse models in consideration of the correlation of propagated errors
Systems and Computers in Japan, 2006Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:We have proposed the forward-Propagation Rule (FP) as an inverse model learning scheme from the viewpoint of biological motor control. This learning scheme is based on a Newton-like method, by which multilayered neural network can acquire an inverse model of the controlled object by a small number of iterative learning trials. There is a problem, however, that the learning procedure, which is characterized by estimation of the supervisor's signal for the input–output signal of the neuron, and also the solution of a linear multiple regression problem for updating the connection weights, is complicated, making it difficult to analyze the learning process. This paper introduces the correlation of the propagated error signal from the viewpoint of the maximum-likelihood method in order to realize a goal-directed learning, which has not hitherto been considered in FP, and extends the learning Rule to the generalized least-square method. As a result, it is clearly shown that the learning Rule in FP is an approximate gradient method. The learning ability of the method is demonstrated by computer simulation. The proposed procedure contains a regularization term derived from the logarithmic likelihood, and the behavior after the convergence of learning exhibits a more stable tendency than in the conventional method. It is also shown that learning can be performed by a simplified method in which the error is simply propagated in the forward direction. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(13): 54–66, 2006; Published online in Wiley InterScience (). DOI 10.1002sscj.20484
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a forward Propagation learning Rule for neural inverse models using a method of recursive least squares
Systems and Computers in Japan, 2005Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:A forward-Propagation learning scheme has been proposed to acquire inverse models of controlled objects in multilayered neural networks. This scheme is quite different from back-Propagation learning Rule. The algorithm of the forward-Propagation Rule consists of two stages. One is the estimation of the instruction signal at each layer by the Newton-like method, and the other is the updating of the connection weights by linear multiple regression. In this scheme, convergence of learning has been faster than other schemes based on backPropagation Rule. However, the problems arise that complex parameters must be set for learning and the learning process is too complex and sometimes stops. This paper proposes to use a method of recursive least squares in the forward-Propagation Rule. The effectiveness of the proposed method is confirmed by computer simulation for the learning of the inverse dynamics model for a two-link arm. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(8): 71–80, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.20237
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a forward Propagation learning Rule for acquiring inverse models in multilayered neural networks
Electronics and Communications in Japan Part Ii-electronics, 2005Co-Authors: Kazuyuki Nagasawa, Naohiro Fukumura, Yoji UnoAbstract:Many proposals have been presented for the acquisition of inverse models in multilayered neural networks. However, most are concerned with the backPropagation Rule or its improvement. In learning in a multilayered neural network based on the backPropagation Rule, there must be a supervisor signal for the output layer, and there must be a particular path to propagate the learning signal in the reverse direction. In addition, convergence is slow due to the use of the method of steepest descent in updating the weights. Consequently, this paper proposes a forward-Propagation Rule in which the neural network model is trained by propagating the motion error exhibited by the control object in the forward direction in the neural network. In the proposed algorithm, the extended Newton's method is used to derive the goal signal (which corresponds to the supervisor signal) in the hidden layer and the output layer. Since linear multiple regression can be used in weight updating for realizing the goal signals, the iteration of weight updating can be reduced compared to the method of steepest descent. A computer simulation was performed for acquisition of a two-link arm model, and the effectiveness of the proposed learning scheme was verified. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(2): 59–68, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20148
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a forward Propagation Rule for acquiring neural inverse models using a rls algorithm
International Conference on Neural Information Processing, 2004Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:It has been suggested that inverse models serve feedforward controllers in the human brain. We have proposed a novel learning scheme to acquire a neural inverse model of a controlled object. This scheme propagates error “forward” in a multi-layered neural network to solve a credit assignment problem based on Newton-like method. In this paper, we apply a RLS algorithm to this scheme for the stability of learning. The suitability of the proposed scheme was confirmed by computer simulation; it could acquire an inverse dynamics model of a 2-link arm faster than a conventional scheme based on a back-Propagation Rule.
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ICONIP - A Forward-Propagation Rule for Acquiring Neural Inverse Models Using a RLS Algorithm
Neural Information Processing, 2004Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:It has been suggested that inverse models serve feedforward controllers in the human brain. We have proposed a novel learning scheme to acquire a neural inverse model of a controlled object. This scheme propagates error “forward” in a multi-layered neural network to solve a credit assignment problem based on Newton-like method. In this paper, we apply a RLS algorithm to this scheme for the stability of learning. The suitability of the proposed scheme was confirmed by computer simulation; it could acquire an inverse dynamics model of a 2-link arm faster than a conventional scheme based on a back-Propagation Rule.
Yoshihiro Ohama - One of the best experts on this subject based on the ideXlab platform.
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a forward Propagation learning Rule for neural inverse models in consideration of the correlation of propagated errors
Systems and Computers in Japan, 2006Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:We have proposed the forward-Propagation Rule (FP) as an inverse model learning scheme from the viewpoint of biological motor control. This learning scheme is based on a Newton-like method, by which multilayered neural network can acquire an inverse model of the controlled object by a small number of iterative learning trials. There is a problem, however, that the learning procedure, which is characterized by estimation of the supervisor's signal for the input–output signal of the neuron, and also the solution of a linear multiple regression problem for updating the connection weights, is complicated, making it difficult to analyze the learning process. This paper introduces the correlation of the propagated error signal from the viewpoint of the maximum-likelihood method in order to realize a goal-directed learning, which has not hitherto been considered in FP, and extends the learning Rule to the generalized least-square method. As a result, it is clearly shown that the learning Rule in FP is an approximate gradient method. The learning ability of the method is demonstrated by computer simulation. The proposed procedure contains a regularization term derived from the logarithmic likelihood, and the behavior after the convergence of learning exhibits a more stable tendency than in the conventional method. It is also shown that learning can be performed by a simplified method in which the error is simply propagated in the forward direction. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(13): 54–66, 2006; Published online in Wiley InterScience (). DOI 10.1002sscj.20484
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a forward Propagation learning Rule for neural inverse models using a method of recursive least squares
Systems and Computers in Japan, 2005Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:A forward-Propagation learning scheme has been proposed to acquire inverse models of controlled objects in multilayered neural networks. This scheme is quite different from back-Propagation learning Rule. The algorithm of the forward-Propagation Rule consists of two stages. One is the estimation of the instruction signal at each layer by the Newton-like method, and the other is the updating of the connection weights by linear multiple regression. In this scheme, convergence of learning has been faster than other schemes based on backPropagation Rule. However, the problems arise that complex parameters must be set for learning and the learning process is too complex and sometimes stops. This paper proposes to use a method of recursive least squares in the forward-Propagation Rule. The effectiveness of the proposed method is confirmed by computer simulation for the learning of the inverse dynamics model for a two-link arm. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(8): 71–80, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.20237
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a forward Propagation Rule for acquiring neural inverse models using a rls algorithm
International Conference on Neural Information Processing, 2004Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:It has been suggested that inverse models serve feedforward controllers in the human brain. We have proposed a novel learning scheme to acquire a neural inverse model of a controlled object. This scheme propagates error “forward” in a multi-layered neural network to solve a credit assignment problem based on Newton-like method. In this paper, we apply a RLS algorithm to this scheme for the stability of learning. The suitability of the proposed scheme was confirmed by computer simulation; it could acquire an inverse dynamics model of a 2-link arm faster than a conventional scheme based on a back-Propagation Rule.
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ICONIP - A Forward-Propagation Rule for Acquiring Neural Inverse Models Using a RLS Algorithm
Neural Information Processing, 2004Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:It has been suggested that inverse models serve feedforward controllers in the human brain. We have proposed a novel learning scheme to acquire a neural inverse model of a controlled object. This scheme propagates error “forward” in a multi-layered neural network to solve a credit assignment problem based on Newton-like method. In this paper, we apply a RLS algorithm to this scheme for the stability of learning. The suitability of the proposed scheme was confirmed by computer simulation; it could acquire an inverse dynamics model of a 2-link arm faster than a conventional scheme based on a back-Propagation Rule.
Naohiro Fukumura - One of the best experts on this subject based on the ideXlab platform.
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a forward Propagation learning Rule for neural inverse models in consideration of the correlation of propagated errors
Systems and Computers in Japan, 2006Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:We have proposed the forward-Propagation Rule (FP) as an inverse model learning scheme from the viewpoint of biological motor control. This learning scheme is based on a Newton-like method, by which multilayered neural network can acquire an inverse model of the controlled object by a small number of iterative learning trials. There is a problem, however, that the learning procedure, which is characterized by estimation of the supervisor's signal for the input–output signal of the neuron, and also the solution of a linear multiple regression problem for updating the connection weights, is complicated, making it difficult to analyze the learning process. This paper introduces the correlation of the propagated error signal from the viewpoint of the maximum-likelihood method in order to realize a goal-directed learning, which has not hitherto been considered in FP, and extends the learning Rule to the generalized least-square method. As a result, it is clearly shown that the learning Rule in FP is an approximate gradient method. The learning ability of the method is demonstrated by computer simulation. The proposed procedure contains a regularization term derived from the logarithmic likelihood, and the behavior after the convergence of learning exhibits a more stable tendency than in the conventional method. It is also shown that learning can be performed by a simplified method in which the error is simply propagated in the forward direction. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(13): 54–66, 2006; Published online in Wiley InterScience (). DOI 10.1002sscj.20484
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a forward Propagation learning Rule for neural inverse models using a method of recursive least squares
Systems and Computers in Japan, 2005Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:A forward-Propagation learning scheme has been proposed to acquire inverse models of controlled objects in multilayered neural networks. This scheme is quite different from back-Propagation learning Rule. The algorithm of the forward-Propagation Rule consists of two stages. One is the estimation of the instruction signal at each layer by the Newton-like method, and the other is the updating of the connection weights by linear multiple regression. In this scheme, convergence of learning has been faster than other schemes based on backPropagation Rule. However, the problems arise that complex parameters must be set for learning and the learning process is too complex and sometimes stops. This paper proposes to use a method of recursive least squares in the forward-Propagation Rule. The effectiveness of the proposed method is confirmed by computer simulation for the learning of the inverse dynamics model for a two-link arm. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(8): 71–80, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.20237
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a forward Propagation learning Rule for acquiring inverse models in multilayered neural networks
Electronics and Communications in Japan Part Ii-electronics, 2005Co-Authors: Kazuyuki Nagasawa, Naohiro Fukumura, Yoji UnoAbstract:Many proposals have been presented for the acquisition of inverse models in multilayered neural networks. However, most are concerned with the backPropagation Rule or its improvement. In learning in a multilayered neural network based on the backPropagation Rule, there must be a supervisor signal for the output layer, and there must be a particular path to propagate the learning signal in the reverse direction. In addition, convergence is slow due to the use of the method of steepest descent in updating the weights. Consequently, this paper proposes a forward-Propagation Rule in which the neural network model is trained by propagating the motion error exhibited by the control object in the forward direction in the neural network. In the proposed algorithm, the extended Newton's method is used to derive the goal signal (which corresponds to the supervisor signal) in the hidden layer and the output layer. Since linear multiple regression can be used in weight updating for realizing the goal signals, the iteration of weight updating can be reduced compared to the method of steepest descent. A computer simulation was performed for acquisition of a two-link arm model, and the effectiveness of the proposed learning scheme was verified. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(2): 59–68, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20148
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a forward Propagation Rule for acquiring neural inverse models using a rls algorithm
International Conference on Neural Information Processing, 2004Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:It has been suggested that inverse models serve feedforward controllers in the human brain. We have proposed a novel learning scheme to acquire a neural inverse model of a controlled object. This scheme propagates error “forward” in a multi-layered neural network to solve a credit assignment problem based on Newton-like method. In this paper, we apply a RLS algorithm to this scheme for the stability of learning. The suitability of the proposed scheme was confirmed by computer simulation; it could acquire an inverse dynamics model of a 2-link arm faster than a conventional scheme based on a back-Propagation Rule.
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ICONIP - A Forward-Propagation Rule for Acquiring Neural Inverse Models Using a RLS Algorithm
Neural Information Processing, 2004Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji UnoAbstract:It has been suggested that inverse models serve feedforward controllers in the human brain. We have proposed a novel learning scheme to acquire a neural inverse model of a controlled object. This scheme propagates error “forward” in a multi-layered neural network to solve a credit assignment problem based on Newton-like method. In this paper, we apply a RLS algorithm to this scheme for the stability of learning. The suitability of the proposed scheme was confirmed by computer simulation; it could acquire an inverse dynamics model of a 2-link arm faster than a conventional scheme based on a back-Propagation Rule.
Zhunga Liu - One of the best experts on this subject based on the ideXlab platform.
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SELP: Semi-supervised evidential label Propagation algorithm for graph data clustering
International Journal of Approximate Reasoning, 2018Co-Authors: Kuang Zhou, Arnaud Martin, Quan Pan, Zhunga LiuAbstract:With the increasing size of social networks in the real world, community detection approaches should be fast and accurate. The label Propagation algorithm is known to be one of the near-linear solutions which is easy to implement. However, it is not stable and it cannot take advantage of the prior information about the network structure which is very common in real applications. In this paper, a new Semi-supervised clustering approach based on an Evidential Label Propagation strategy (SELP) is proposed to incorporate limited domain knowledge into the community detection model. The main advantage of SELP is that it can effectively use limited supervised information to guide the detection process. The prior information about the labels of nodes in the graph, including the labeled nodes and the unlabeled ones, is initially expressed in the form of mass functions. Then the evidential label Propagation Rule is designed to propagate the labels from the labeled nodes to the unlabeled ones. The communities of each node can be identified after the Propagation process becomes stable. The outliers can be identified to be in a special class. Experimental results demonstrate the effectiveness of SELP on both graphs and classical data sets.
Kuang Zhou - One of the best experts on this subject based on the ideXlab platform.
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SELP: Semi-supervised evidential label Propagation algorithm for graph data clustering
International Journal of Approximate Reasoning, 2018Co-Authors: Kuang Zhou, Arnaud Martin, Quan Pan, Zhunga LiuAbstract:With the increasing size of social networks in the real world, community detection approaches should be fast and accurate. The label Propagation algorithm is known to be one of the near-linear solutions which is easy to implement. However, it is not stable and it cannot take advantage of the prior information about the network structure which is very common in real applications. In this paper, a new Semi-supervised clustering approach based on an Evidential Label Propagation strategy (SELP) is proposed to incorporate limited domain knowledge into the community detection model. The main advantage of SELP is that it can effectively use limited supervised information to guide the detection process. The prior information about the labels of nodes in the graph, including the labeled nodes and the unlabeled ones, is initially expressed in the form of mass functions. Then the evidential label Propagation Rule is designed to propagate the labels from the labeled nodes to the unlabeled ones. The communities of each node can be identified after the Propagation process becomes stable. The outliers can be identified to be in a special class. Experimental results demonstrate the effectiveness of SELP on both graphs and classical data sets.
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Semi-supervised evidential label Propagation algorithm for graph data
2016Co-Authors: Kuang Zhou, Arnaud Martin, Quan PanAbstract:In the task of community detection, there often exists some useful prior information. In this paper, a Semi-supervised clustering approach using a new Evidential Label Propagation strategy (SELP) is proposed to incorporate the domain knowledge into the community detection model. The main advantage of SELP is that it can take limited supervised knowledge to guide the detection process. The prior information of community labels is expressed in the form of mass functions initially. Then a new evidential label Propagation Rule is adopted to propagate the labels from labeled data to unlabeled ones. The outliers can be identified to be in a special class. The experimental results demonstrate the effectiveness of SELP.
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Belief relational clustering and its application to community detection
2016Co-Authors: Kuang ZhouAbstract:Communities are groups of nodes (vertices) which probably share common properties and/or play similar roles within the graph. They can extract specific structures from complex networks, and consequently community detection has attracted considerable attention crossing many areas where systems are often represented as graphs. We consider in this work to represent graphs as relational data, and propose models for the corresponding relational data clustering. Four approaches are brought forward to handle the community detection problem under different scenarios. We start with a basic situation where nodes in the graph are clustered based on the dissimilarities and propose a new c-partition clustering approach named Median Evidential C-Means (MECM) algorithm. This approach extends the median clustering methods in the framework of belief function theory. Moreover, a community detection scheme based on MECM is presented. The proposed approach could provide credal partitions for data sets with only known dissimilarities. The dissimilarity measure could be neither symmetric nor fulfilling any metric requirements. It is only required to be of intuitive meaning. Thus it expands application scope of credal partitions. In order to capture various aspects of the community structures, we may need more members rather than one to be referred as the prototypes of an individual group. Motivated by this idea, a Similarity-based Multiple Prototype (SMP) community detection approach is proposed. The centrality values are used as the criterion to select multiple prototypes to characterize each community. The prototype weights are derived to describe the degree of representativeness of objects for their own communities. Then the similarity between each node and community is defined, and the nodes are partitioned into divided communities according to these similarities. Crisp and fuzzy partitions could be obtained by the application of SMP. Following, we extend SMP in the framework of belief functions to get credal partitions so that we can gain a better understanding of the data structure. The prototype weights are incorporate into the objective function of evidential clustering. The mass membership and the prototype weights could be updated alternatively during the optimization process. In this case, each cluster could be described using multiple weighted prototypes. As we will show, the prototype weights could also provide us some useful information for structure analysis of the data sets. Lastly, the original update Rule and Propagation criterion of LPA are extended in the framework of belief functions. A new community detection approach, called Semi-supervised Evidential Label Propagation (SELP), is proposed as an enhanced version of the conventional LPA. One of the advantages of SELP is that it could take use of the available prior knowledge about the community labels of some individuals. This is very common in real practice. In SELP, the nodes are divided into two parts. One contains the labeled nodes, and the other includes the unlabeled ones. The community labels are propagated from the labeled nodes to the unlabeled ones step by step according to the proposed evidential label Propagation Rule. The performance of the proposed approaches is evaluated using benchmark graph data sets and generated graphs. Our experimental results illustrate the effectiveness of the proposed clustering algorithms and community detection approaches.
