scholarly journals Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Takashi Kuremoto ◽  
Takahito Komoto ◽  
Kunikazu Kobayashi ◽  
Masanao Obayashi
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Learning System ◽  
Automatic Tuning ◽  
Self Organizing Map ◽  
Multiple Languages ◽  
Voice Instruction ◽  
Japanese English ◽  
Neighborhood Preservation ◽  
Self Organizing

An improved self-organizing map (SOM), parameterless-growing-SOM (PL-G-SOM), is proposed in this paper. To overcome problems existed in traditional SOM (Kohonen, 1982), kinds of structure-growing-SOMs or parameter-adjusting-SOMs have been invented and usually separately. Here, we combine the idea of growing SOMs (Bauer and Villmann, 1997; Dittenbach et al. 2000) and a parameterless SOM (Berglund and Sitte, 2006) together to be a novel SOM named PL-G-SOM to realize additional learning, optimal neighborhood preservation, and automatic tuning of parameters. The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. The policy is adjusted by the reward/punishment given by the user of the robot. A feeling map is also designed to express learning degrees of voice instructions. Learning and additional learning experiments used instructions in multiple languages including Japanese, English, Chinese, and Malaysian confirmed the effectiveness of our proposed system.

scholarly journals Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kwang Baek Kim ◽  
Chang Won Kim
Keyword(s):  
Fatty Liver ◽  
Hepatic Steatosis ◽  
Learning Algorithm ◽  
Fat Content ◽  
Liver Fat ◽  
Self Organizing Map ◽  
Liver Fat Content ◽  
Computer Aided ◽  
Good Set ◽  
Self Organizing

Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.

scholarly journals Self-organizing maps for storage and transfer of knowledge in reinforcement learning

2018 ◽  
Vol 27 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Thommen George Karimpanal ◽  
Roland Bouffanais
Keyword(s):  
Reinforcement Learning ◽  
Self Organizing Map ◽  
Value Functions ◽  
Transfer Of Knowledge ◽  
Network Growth ◽  
Self Organizing Maps ◽  
Task Knowledge ◽  
Novel Approach ◽  
Learning Agent ◽  
Self Organizing

The idea of reusing or transferring information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency of a reinforcement learning agent. In this work, we describe a novel approach for reusing previously acquired knowledge by using it to guide the exploration of an agent while it learns new tasks. In order to do so, we employ a variant of the growing self-organizing map algorithm, which is trained using a measure of similarity that is defined directly in the space of the vectorized representations of the value functions. In addition to enabling transfer across tasks, the resulting map is simultaneously used to enable the efficient storage of previously acquired task knowledge in an adaptive and scalable manner. We empirically validate our approach in a simulated navigation environment and also demonstrate its utility through simple experiments using a mobile micro-robotics platform. In addition, we demonstrate the scalability of this approach and analytically examine its relation to the proposed network growth mechanism. Furthermore, we briefly discuss some of the possible improvements and extensions to this approach, as well as its relevance to real-world scenarios in the context of continual learning.

Reinforcement Learning Applied to a Differential Game

1995 ◽  
Vol 4 (1) ◽  
pp. 3-28 ◽  
Author(s):  
Mance E. Harmon ◽  
Leemon C. Baird ◽  
A. Harry Klopf
Keyword(s):  
Reinforcement Learning ◽  
Differential Game ◽  
Learning Algorithm ◽  
Learning System ◽  
Test Bed ◽  
Linear Quadratic ◽  
Time Step ◽  
Q Learning ◽  
Step Duration ◽  
Markov Decision

An application of reinforcement learning to a linear-quadratic, differential game is presented. The reinforcement learning system uses a recently developed algorithm, the residual-gradient form of advantage updating. The game is a Markov decision process with continuous time, states, and actions, linear dynamics, and a quadratic cost function. The game consists of two players, a missile and a plane; the missile pursues the plane and the plane evades the missile. Although a missile and plane scenario was the chosen test bed, the reinforcement learning approach presented here is equally applicable to biologically based systems, such as a predator pursuing prey. The reinforcement learning algorithm for optimal control is modified for differential games to find the minimax point rather than the maximum. Simulation results are compared to the analytical solution, demonstrating that the simulated reinforcement learning system converges to the optimal answer. The performance of both the residual-gradient and non-residual-gradient forms of advantage updating and Q-learning are compared, demonstrating that advantage updating converges faster than Q-learning in all simulations. Advantage updating also is demonstrated to converge regardless of the time step duration; Q-learning is unable to converge as the time step duration grows small.

A teaching method using a self-organizing map for reinforcement learning

2004 ◽  
Vol 7 (4) ◽  
pp. 193-197 ◽  
Author(s):  
Takeshi Tateyama ◽  
Seiichi Kawata ◽  
Toshiki Oguchi
Keyword(s):  
Reinforcement Learning ◽  
Teaching Method ◽  
Self Organizing Map ◽  
Self Organizing

A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

2008 ◽  
Vol 07 (04) ◽  
pp. 363-383 ◽  
Author(s):  
MUSTAPHA LEBBAH ◽  
YOUNÈS BENNANI ◽  
NICOLETA ROGOVSCHI
Keyword(s):  
Binary Data ◽  
Learning Algorithm ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Binary Coding ◽  
Public Data ◽  
Multivariate Binary Data ◽  
Self Organizing

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

Self-Organizing Map Learning Nonlinearly Embedded Manifolds

2005 ◽  
Vol 4 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Timo Similä
Keyword(s):  
Learning Algorithm ◽  
Image Data ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Complex Data ◽  
Dimensional Manifold ◽  
Self Organizing Map ◽  
Training Strategy ◽  
Low Dimensional ◽  
Self Organizing

One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.

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