Distance Learning Environment for Internet Developing Areas Using the Satellite Link

Mikawa, Shoko,Takei, Jun,Okawa, Keiko,Murai, Jun 통신위성우주산업연구회 2003 Joint Conference on Satellite Communications Vol.2003 No.-

This paper propose a distance learning environment using the Internet on satellite link where the Internet infrastructure is insufficient. We call these areas "Internet developing areas" in our paper. The merit of distance learning is that students can receive cutting edge lectures from professors all over the world. However, Internet developing areas have difficulty to receive such lectures through the Internet. We proposed a distance learning environment that can be applied to these areas using a satellite link as an Internet infrastructure. As we wanted to deliver lectures to as many places at low cost, we proposed to use the satellite link as receive only link to deliver a lecture, and use the local Internet connection for realtime feedback from the students. We conducted 45 lectures including 5 lecture, courses as proof experiments, and evaluated our system. As result, new and effective distance learning environment was proposed, and educational collaborations are made possible.

An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification

Mikawa, Kenta,Ishida, Takashi,Goto, Masayuki Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.1

This paper discusses a new weighting method for text analyzing from the view point of supervised learning. The term frequency and inverse term frequency measure (tf-idf measure) is famous weighting method for information retrieval, and this method can be used for text analyzing either. However, it is an experimental weighting method for information retrieval whose effectiveness is not clarified from the theoretical viewpoints. Therefore, other effective weighting measure may be obtained for document classification problems. In this study, we propose the optimal weighting method for document classification problems from the view point of supervised learning. The proposed measure is more suitable for the text classification problem as used training data than the tf-idf measure. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of newspaper article and the customer review which is posted on the web site.

An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification

Kenta Mikawa,Takashi Ishida,Masayuki Goto 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.1

This paper discusses a new weighting method for text analyzing from the view point of supervised learning. The term frequency and inverse term frequency measure (tf-idf measure) is famous weighting method for information retrieval, and this method can be used for text analyzing either. However, it is an experimental weighting method for information retrieval whose effectiveness is not clarified from the theoretical viewpoints. Therefore, other effective weighting measure may be obtained for document classification problems. In this study, we propose the optimal weighting method for document classification problems from the view point of supervised learning. The proposed measure is more suitable for the text classification problem as used training data than the tf-idf measure. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of newspaper article and the customer review which is posted on the web site.

Memory System Controlled by Mathematical AIM Model for Robot Vision Equipped with Sleep andWake Functions

Masahiko Mikawa,Masahiro Yoshikawa,Takeshi Tsujimura 제어로봇시스템학회 2006 제어로봇시스템학회 국제학술대회 논문집 Vol.2006 No.10

Librarian Robot Controlled by Mathematical AIM Model

Masahiko Mikawa,Masahiro Yoshikawa,Takeshi Tsujimura,Kazuyo Tanaka 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

This paper presents a librarian robot that has sleep and wake functions. This robot is equipped with a laser range finder for tracking library users’ behaviors, a microphone for conversation with a user and a stereo vision. A lot of processes run in parallel in this system, operations of these processes are controlled by our proposed mathematical Activation-Input-Modulation (AIM) model, that can express consciousness states, such as wake or sleep based on stimuli detected by the external sensors. In a waking state, sensory information with stimuli is mainly processed. In a sleep state, most processing is paused, or information stored in memories during waking is mainly processed. Moreover, this system has two kinds of memories. One is a memory stored when external stimuli are detected, the other is a memory stored when no stimulus is detected. Both dynamic and gradual changes of sensory information can be stored by these functions.

Multi-Valued Classification of Text Data Based on an ECOC Approach Using a Ternary Orthogonal Table

Suzuki, Leona,Mikawa, Kenta,Goto, Masayuki Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.2

Because of the advancements in information technology, a large number of document data has been accumulated on various databases and automatic multi-valued classification becomes highly relevant. This paper focuses on a multi-valued classification technique that is based on Error-Correcting Output Codes (ECOC) and which combines several binary classifiers. When predicting the category of a new document data, the outputs of the binary classifiers are combined to produce a predicted value. It is a known problem that if two category sets have an imbalanced amount of training data, the prediction accuracy of a binary classifier is low. To solve this problem, a previous study proposed to employ the Reed-Muller (RM) codes in the context an ECOC approach for resolving the imbalance in the cardinality of the training data sets. However, RM codes can equalize the amount of between training data of two category sets only for a specific number of categories. We want to provide a method that can be employed for a multi-valued classification with an arbitrary number of categories. In this paper, we propose a new configuration method combining binary classifiers with categories, which are not used for classification. This method allows us to reduce the amount of training data for each binary classifier while improving the balance of the training data between two category sets for each binary classifier. As a result, the computational complexity can be decreased. We verify the effectiveness of our proposed method by conducting a document classification experiment.

Adaptive Prediction Method Based on Alternating Decision Forests with Considerations for Generalization Ability

Misawa, Shotaro,Mikawa, Kenta,Goto, Masayuki Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.3

Many machine learning algorithms have been proposed and applied to a wide range of prediction problems in the field of industrial management. Lately, the amount of data is increasing and machine learning algorithms with low computational costs and efficient ensemble methods are needed. Alternating Decision Forest (ADF) is an efficient ensemble method known for its high performance and low computational costs. ADFs introduce weights representing the degree of prediction accuracy for each piece of training data and randomly select attribute variables for each node. This method can effectively construct an ensemble model that can predict training data accurately while allowing each decision tree to retain different features. However, outliers can cause overfitting, and since candidates of branch conditions vary for nodes in ADFs, there is a possibility that prediction accuracy will deteriorate because the fitness of training data is highly restrained. In order to improve prediction accuracy, we focus on the prediction results for new data. That is to say, we introduce bootstrap sampling so that the algorithm can generate out-of-bag (OOB) datasets for each tree in the training phase. Additionally, we construct an effective ensemble of decision trees to improve generalization ability by considering the prediction accuracy for OOB data. To verify the effectiveness of the proposed method, we conduct simulation experiments using the UCI machine learning repository. This method provides robust and accurate predictions for datasets with many attribute variables.

A Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics

Yamamoto, Yusei,Mikawa, Kenta,Goto, Masayuki Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.2

With rapid development on information society, automatic document classification by machine learning has become even more important. In document classification, it is assumed that a new input data can be classified into any of the categories observed in the training data. Therefore, if a new input data belongs to an unobserved category which does not exist in the training data, then such data cannot be classified exactly. To solve the above problem, Arakawa et al. proposed the method which models the generative probabilities of documents with a mixture of Polya distributions and estimates the optimum category within all observed and unobserved categories where it is assumed that documents in each category are generated from each single Polya distribution. However, the statistical characteristics of document categories are generally more complicated and there are various underlying latent topics in a category. Because a single Polya distribution models each category in the conventional approach, this method cannot represent the variation of word frequency depending on plural unobserved latent topics. This paper proposes a new model which assumes a mixture of Polya distributions for the generative probabilities of documents in a category to represent plural latent topics. To verify the effectiveness of the proposed method, we conduct the simulation experiments of document classification by using a set of English newspaper articles.

A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites

Goto, Masayuki,Mikawa, Kenta,Hirasawa, Shigeichi,Kobayashi, Manabu,Suko, Tota,Horii, Shunsuke Korean Institute of Industrial Engineers 2015 Industrial Engineeering & Management Systems Vol.14 No.4

The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

Real-Time Hand Motion Estimation Using EMG Signals with Support Vector Machines

Masahiro Yoshikawa,Masahiko Mikawa,Kazuyo Tanaka 제어로봇시스템학회 2006 제어로봇시스템학회 국제학술대회 논문집 Vol.2006 No.10