The Experts below are selected from a list of 28455 Experts worldwide ranked by ideXlab platform
Shingo Mabu - One of the best experts on this subject based on the ideXlab platform.
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RP-AG-SOM: A Growing Self-organizing Map with Assymetric Neighborhood Function and Variable Radius
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016, 2017Co-Authors: Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Yuya Kuzukami, Kunikazu KobayashiAbstract:To deal with the problem of unit exhaustion and high computational cost of Kohonen’s Self-Organizing Map (SOM), Growing SOM was proposed in last century. Meanwhile, to avoid topological twist of the map, Aoki and Aoyagi proposed an asymmetric Neighborhood Function to instead of the conventional symmetric one. Furthermore, Masuda et al. proposed a heuristic method named Radius Parallel Self-Organizing Map to decide the adaptive bounds of SOM during learning process. In this paper, we propose to compose these 3 methods to construct a novel efficient SOM named RP-AG-SOM: a Growing Self-Organizing Map with asymmetric Neighborhood Function and Variable Radius. Additionally, RP-AG-SOM is applied to a voice instruction recognition system successfully.
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A hand shape instruction recognition and learning system using growing SOM with asymmetric Neighborhood Function
Neurocomputing, 2016Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:Abstract For Human–Machine Interaction systems, it is a convenient method to send user׳s instructions to robots, TV sets, and other electronic equipments by showing different shapes of a hand of user. In our previous works, we proposed to use improved Kohonen׳s Self-Organizing Maps (SOMs), i.e., Transient-SOM (T-SOM) and Parameterless Growing SOM (PL-G-SOM) to recognize different patterns of hand shapes given by different bendings of five fingers of a hand. Recently, an asymmetric Neighborhood Function was proposed and introduced into the conventional SOM to improve the learning performance by Aoki and Aoyagi. In this paper, we propose to employ their asymmetric Neighborhood Function into Growing SOM (GSOM), which is an improved SOM to deal with additional online learning for input data. Furthermore, the improved GSOM is applied to a hand shape recognition and instruction learning system, and the results of experiments with eight kinds of instructions showed the effectiveness of the proposed system.
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a hand shape instruction recognition and learning system using growing som with asymmetric Neighborhood Function
International Conference on Intelligent Computing, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:In this paper, we adopt an asymmetric Neighborhood Function proposed by Aoki and Aoyagi in to a PL-G-SOM to improve the learning performance of the hand shape instruction perspective and learning system. The asymmetric Neighborhood Function was used in a normal SOM and few applications can be found. The novel PL-G-SOM and its improved version are named as "AGSOM" and “IAGSOM” respectively. The effectiveness of the proposed method was confirmed by the experiments with 8 kinds of instructions.
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ICIC (1) - A Hand Shape Instruction Recognition and Learning System Using Growing SOM with Asymmetric Neighborhood Function
Intelligent Computing Theory, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:In this paper, we adopt an asymmetric Neighborhood Function proposed by Aoki and Aoyagi in to a PL-G-SOM to improve the learning performance of the hand shape instruction perspective and learning system. The asymmetric Neighborhood Function was used in a normal SOM and few applications can be found. The novel PL-G-SOM and its improved version are named as "AGSOM" and “IAGSOM” respectively. The effectiveness of the proposed method was confirmed by the experiments with 8 kinds of instructions.
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One-D-R-A-G-SOM and its Application to a Hand Shape Instruction Learning System
International Journal of Networked and Distributed Computing, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Shingo Mabu, Kunikazu KobayashiAbstract:In this paper, a novel self-organizing map (SOM) named “One-D-R-A-G-SOM” is proposed. It is a kind of one dimensional ring type growing SOM using asymmetric Neighborhood Function. As the topology of one dimensional ring type feature map is more suitable to increase or decrease the number of units, and the disorder of the map is available to be solved by the asymmetric Neighborhood Function, the proposed model gives priority of learning performance to the conventional two dimensional growing SOM. Additionally, One-D-R-A-G-SOM is introduced to a hand shape recognition and instruction learning system. Experiment results showed the effectiveness of the novel system comparing with systems using the conventional SOMs.
Xiuguo Liu - One of the best experts on this subject based on the ideXlab platform.
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Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images
Remote Sensing, 2016Co-Authors: Qihao Chen, Shuai Yang, Xiuguo LiuAbstract:Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a Neighborhood Function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined Neighborhood Function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods.
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IGARSS - Polarimetric SAR images classification based on L distribution and spatial context
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Qihao Chen, Xiaoli Xing, Shuai Yang, Xiuguo LiuAbstract:To obtain accurate classification results of polarimetric SAR images in different heterogeneity areas, a novel unsupervised classification method is proposed, which combines an advanced distribution with spatial contextual information based on stochastic expectation maximization (SEM) algorithm. Specifically, the probabilities of class membership are calculated by L distribution, and a Neighborhood Function is defined to describe spatial contextual information. Then the probabilities of class membership are altered by the predefined Neighborhood Function via probabilistic label relaxation (PLR) technique. Moreover, RADARSAT-2 and EMISAR data are used to verify the effectiveness of the proposed method. The experiment results show it can accurately classify different heterogeneity areas and obtain more consistent results compared with other algorithms.
Takashi Kuremoto - One of the best experts on this subject based on the ideXlab platform.
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RP-AG-SOM: A Growing Self-organizing Map with Assymetric Neighborhood Function and Variable Radius
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016, 2017Co-Authors: Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Yuya Kuzukami, Kunikazu KobayashiAbstract:To deal with the problem of unit exhaustion and high computational cost of Kohonen’s Self-Organizing Map (SOM), Growing SOM was proposed in last century. Meanwhile, to avoid topological twist of the map, Aoki and Aoyagi proposed an asymmetric Neighborhood Function to instead of the conventional symmetric one. Furthermore, Masuda et al. proposed a heuristic method named Radius Parallel Self-Organizing Map to decide the adaptive bounds of SOM during learning process. In this paper, we propose to compose these 3 methods to construct a novel efficient SOM named RP-AG-SOM: a Growing Self-Organizing Map with asymmetric Neighborhood Function and Variable Radius. Additionally, RP-AG-SOM is applied to a voice instruction recognition system successfully.
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A hand shape instruction recognition and learning system using growing SOM with asymmetric Neighborhood Function
Neurocomputing, 2016Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:Abstract For Human–Machine Interaction systems, it is a convenient method to send user׳s instructions to robots, TV sets, and other electronic equipments by showing different shapes of a hand of user. In our previous works, we proposed to use improved Kohonen׳s Self-Organizing Maps (SOMs), i.e., Transient-SOM (T-SOM) and Parameterless Growing SOM (PL-G-SOM) to recognize different patterns of hand shapes given by different bendings of five fingers of a hand. Recently, an asymmetric Neighborhood Function was proposed and introduced into the conventional SOM to improve the learning performance by Aoki and Aoyagi. In this paper, we propose to employ their asymmetric Neighborhood Function into Growing SOM (GSOM), which is an improved SOM to deal with additional online learning for input data. Furthermore, the improved GSOM is applied to a hand shape recognition and instruction learning system, and the results of experiments with eight kinds of instructions showed the effectiveness of the proposed system.
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a hand shape instruction recognition and learning system using growing som with asymmetric Neighborhood Function
International Conference on Intelligent Computing, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:In this paper, we adopt an asymmetric Neighborhood Function proposed by Aoki and Aoyagi in to a PL-G-SOM to improve the learning performance of the hand shape instruction perspective and learning system. The asymmetric Neighborhood Function was used in a normal SOM and few applications can be found. The novel PL-G-SOM and its improved version are named as "AGSOM" and “IAGSOM” respectively. The effectiveness of the proposed method was confirmed by the experiments with 8 kinds of instructions.
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ICIC (1) - A Hand Shape Instruction Recognition and Learning System Using Growing SOM with Asymmetric Neighborhood Function
Intelligent Computing Theory, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:In this paper, we adopt an asymmetric Neighborhood Function proposed by Aoki and Aoyagi in to a PL-G-SOM to improve the learning performance of the hand shape instruction perspective and learning system. The asymmetric Neighborhood Function was used in a normal SOM and few applications can be found. The novel PL-G-SOM and its improved version are named as "AGSOM" and “IAGSOM” respectively. The effectiveness of the proposed method was confirmed by the experiments with 8 kinds of instructions.
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One-D-R-A-G-SOM and its Application to a Hand Shape Instruction Learning System
International Journal of Networked and Distributed Computing, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Shingo Mabu, Kunikazu KobayashiAbstract:In this paper, a novel self-organizing map (SOM) named “One-D-R-A-G-SOM” is proposed. It is a kind of one dimensional ring type growing SOM using asymmetric Neighborhood Function. As the topology of one dimensional ring type feature map is more suitable to increase or decrease the number of units, and the disorder of the map is available to be solved by the asymmetric Neighborhood Function, the proposed model gives priority of learning performance to the conventional two dimensional growing SOM. Additionally, One-D-R-A-G-SOM is introduced to a hand shape recognition and instruction learning system. Experiment results showed the effectiveness of the novel system comparing with systems using the conventional SOMs.
Toshio Aoyagi - One of the best experts on this subject based on the ideXlab platform.
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Asymmetric Neighborhood Functions accelerate ordering process of self-organizing maps.
Physical Review E, 2011Co-Authors: Kaiichiro Ota, Takaaki Aoki, Koji Kurata, Toshio AoyagiAbstract:A self-organizing map (SOM) algorithm can generate a topographic map from a high-dimensional stimulus space to a low-dimensional array of units. Because a topographic map preserves Neighborhood relationships between the stimuli, the SOM can be applied to certain types of information processing such as data visualization. During the learning process, however, topological defects frequently emerge in the map. The presence of defects tends to drastically slow down the formation of a globally ordered topographic map. To remove such topological defects, it has been reported that an asymmetric Neighborhood Function is effective, but only in the simple case of mapping one-dimensional stimuli to a chain of units. In this paper, we demonstrate that even when high-dimensional stimuli are used, the asymmetric Neighborhood Function is effective for both artificial and real-world data. Our results suggest that applying the asymmetric Neighborhood Function to the SOM algorithm improves the reliability of the algorithm. In addition, it enables processing of complicated, high-dimensional data by using this algorithm.
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Ordering process of self-organizing maps improved by asymmetric Neighborhood Function
Cognitive Neurodynamics, 2009Co-Authors: Takaaki Aoki, Kaiichiro Ota, Koji Kurata, Toshio AoyagiAbstract:The Self-organizing map (SOM) is an unsupervised learning method based on the neural computation, which has found wide applications. However, the learning process sometime takes multi-stable states, within which the map is trapped to an undesirable disordered state including topological defects on the map. These topological defects critically aggravate the performance of the SOM. In order to overcome this problem, we propose to introduce an asymmetric Neighborhood Function for the SOM algorithm. Compared with the conventional symmetric one, the asymmetric Neighborhood Function accelerates the ordering process even in the presence of the defect. However, this asymmetry tends to generate a distorted map. This can be suppressed by an improved method of the asymmetric Neighborhood Function. In the case of one-dimensional SOM, it is found that the required steps for perfect ordering is numerically shown to be reduced from O ( N ^3) to O ( N ^2). We also discuss the ordering process of a twisted state in two-dimensional SOM, which can not be rectified by the ordinary symmetric Neighborhood Function.
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ordering process of self organizing maps improved by asymmetric Neighborhood Function
International Conference on Neural Information Processing, 2007Co-Authors: Takaaki Aoki, Kaiichiro Ota, Koji Kurata, Toshio AoyagiAbstract:The Self-Organizing Map (SOM) is an unsupervised learning method based on the neural computation, which has recently found wide applications. However, the learning process sometime takes multi-stable states, within which the map is trapped to a undesirable disordered state including topological defects on the map. These topological defects critically aggravate the performance of the SOM. In order to overcome this problem, we propose to introduce an asymmetric Neighborhood Function for the SOM algorithm. Compared with the conventional symmetric one, the asymmetric Neighborhood Function accelerates the ordering process even in the presence of the defect. However, this asymmetry tends to generate a distorted map. This can be suppressed by an improved method of the asymmetric Neighborhood Function. In the case of one-dimensional SOM, it found that the required steps for perfect ordering is numerically shown to be reduced from O(N3) to O(N2).
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Self-Organizing Maps with Asymmetric Neighborhood Function
Neural computation, 2007Co-Authors: Takaaki Aoki, Toshio AoyagiAbstract:The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric Neighborhood Function be used for the SOM algorithm. Compared with the commonly used symmetric Neighborhood Function, we found that an asymmetric Neighborhood Function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric Neighborhood Function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N3) to O(N2) with an asymmetric Neighborhood Function, even when the improved algorithm is used to get the final map without distortion.
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ICONIP (1) - Ordering Process of Self-Organizing Maps Improved by Asymmetric Neighborhood Function
Neural Information Processing, 1Co-Authors: Takaaki Aoki, Kaiichiro Ota, Koji Kurata, Toshio AoyagiAbstract:The Self-Organizing Map (SOM) is an unsupervised learning method based on the neural computation, which has recently found wide applications. However, the learning process sometime takes multi-stable states, within which the map is trapped to a undesirable disordered state including topological defects on the map. These topological defects critically aggravate the performance of the SOM. In order to overcome this problem, we propose to introduce an asymmetric Neighborhood Function for the SOM algorithm. Compared with the conventional symmetric one, the asymmetric Neighborhood Function accelerates the ordering process even in the presence of the defect. However, this asymmetry tends to generate a distorted map. This can be suppressed by an improved method of the asymmetric Neighborhood Function. In the case of one-dimensional SOM, it found that the required steps for perfect ordering is numerically shown to be reduced from O(N3) to O(N2).
Kunikazu Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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RP-AG-SOM: A Growing Self-organizing Map with Assymetric Neighborhood Function and Variable Radius
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016, 2017Co-Authors: Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Yuya Kuzukami, Kunikazu KobayashiAbstract:To deal with the problem of unit exhaustion and high computational cost of Kohonen’s Self-Organizing Map (SOM), Growing SOM was proposed in last century. Meanwhile, to avoid topological twist of the map, Aoki and Aoyagi proposed an asymmetric Neighborhood Function to instead of the conventional symmetric one. Furthermore, Masuda et al. proposed a heuristic method named Radius Parallel Self-Organizing Map to decide the adaptive bounds of SOM during learning process. In this paper, we propose to compose these 3 methods to construct a novel efficient SOM named RP-AG-SOM: a Growing Self-Organizing Map with asymmetric Neighborhood Function and Variable Radius. Additionally, RP-AG-SOM is applied to a voice instruction recognition system successfully.
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A hand shape instruction recognition and learning system using growing SOM with asymmetric Neighborhood Function
Neurocomputing, 2016Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:Abstract For Human–Machine Interaction systems, it is a convenient method to send user׳s instructions to robots, TV sets, and other electronic equipments by showing different shapes of a hand of user. In our previous works, we proposed to use improved Kohonen׳s Self-Organizing Maps (SOMs), i.e., Transient-SOM (T-SOM) and Parameterless Growing SOM (PL-G-SOM) to recognize different patterns of hand shapes given by different bendings of five fingers of a hand. Recently, an asymmetric Neighborhood Function was proposed and introduced into the conventional SOM to improve the learning performance by Aoki and Aoyagi. In this paper, we propose to employ their asymmetric Neighborhood Function into Growing SOM (GSOM), which is an improved SOM to deal with additional online learning for input data. Furthermore, the improved GSOM is applied to a hand shape recognition and instruction learning system, and the results of experiments with eight kinds of instructions showed the effectiveness of the proposed system.
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a hand shape instruction recognition and learning system using growing som with asymmetric Neighborhood Function
International Conference on Intelligent Computing, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:In this paper, we adopt an asymmetric Neighborhood Function proposed by Aoki and Aoyagi in to a PL-G-SOM to improve the learning performance of the hand shape instruction perspective and learning system. The asymmetric Neighborhood Function was used in a normal SOM and few applications can be found. The novel PL-G-SOM and its improved version are named as "AGSOM" and “IAGSOM” respectively. The effectiveness of the proposed method was confirmed by the experiments with 8 kinds of instructions.
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ICIC (1) - A Hand Shape Instruction Recognition and Learning System Using Growing SOM with Asymmetric Neighborhood Function
Intelligent Computing Theory, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo MabuAbstract:In this paper, we adopt an asymmetric Neighborhood Function proposed by Aoki and Aoyagi in to a PL-G-SOM to improve the learning performance of the hand shape instruction perspective and learning system. The asymmetric Neighborhood Function was used in a normal SOM and few applications can be found. The novel PL-G-SOM and its improved version are named as "AGSOM" and “IAGSOM” respectively. The effectiveness of the proposed method was confirmed by the experiments with 8 kinds of instructions.
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One-D-R-A-G-SOM and its Application to a Hand Shape Instruction Learning System
International Journal of Networked and Distributed Computing, 2014Co-Authors: Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Shingo Mabu, Kunikazu KobayashiAbstract:In this paper, a novel self-organizing map (SOM) named “One-D-R-A-G-SOM” is proposed. It is a kind of one dimensional ring type growing SOM using asymmetric Neighborhood Function. As the topology of one dimensional ring type feature map is more suitable to increase or decrease the number of units, and the disorder of the map is available to be solved by the asymmetric Neighborhood Function, the proposed model gives priority of learning performance to the conventional two dimensional growing SOM. Additionally, One-D-R-A-G-SOM is introduced to a hand shape recognition and instruction learning system. Experiment results showed the effectiveness of the novel system comparing with systems using the conventional SOMs.
