The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform

Koji Koyamada - One of the best experts on this subject based on the ideXlab platform.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2018
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    TheEbara, Yasuo beginner counselorsHayashida, YumahaveUetsuji, TomoyadifficultyKoyamada, Koji doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2017
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    The beginner counselors have difficulty doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • category classification of text data with machine learning technique for visualizing flow of conversation in counseling
    2017 Nicograph International (NicoInt), 2017
    Co-Authors: Yuma Hayashida, Tomoya Uetsuji, Yasuo Ebara, Koji Koyamada
    Abstract:

    The beginner counselors have more likely to continue counseling in their own interest, they have a high tendency to make great use of the Closed-Ended Question in order to confirm the interpretation with the client. While expert counselors are instructing the counseling skill to beginner counselors, we consider that the reaction of a client for a beginner counselor's Question is important to visualize in an appropriate method. To respond the request, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation data is very low in the current system. To improve this problem, we have implemented on the category classification method of text data with SVM (Support Vector Machine) as machine learning technique to visualize the flow of conversation in counseling. In addition, we have compared and evaluated with results of the initial classification method of the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the results in the current system.

Yasuo Ebara - One of the best experts on this subject based on the ideXlab platform.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2018
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    TheEbara, Yasuo beginner counselorsHayashida, YumahaveUetsuji, TomoyadifficultyKoyamada, Koji doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2017
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    The beginner counselors have difficulty doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • category classification of text data with machine learning technique for visualizing flow of conversation in counseling
    2017 Nicograph International (NicoInt), 2017
    Co-Authors: Yuma Hayashida, Tomoya Uetsuji, Yasuo Ebara, Koji Koyamada
    Abstract:

    The beginner counselors have more likely to continue counseling in their own interest, they have a high tendency to make great use of the Closed-Ended Question in order to confirm the interpretation with the client. While expert counselors are instructing the counseling skill to beginner counselors, we consider that the reaction of a client for a beginner counselor's Question is important to visualize in an appropriate method. To respond the request, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation data is very low in the current system. To improve this problem, we have implemented on the category classification method of text data with SVM (Support Vector Machine) as machine learning technique to visualize the flow of conversation in counseling. In addition, we have compared and evaluated with results of the initial classification method of the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the results in the current system.

Yuma Hayashida - One of the best experts on this subject based on the ideXlab platform.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2018
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    TheEbara, Yasuo beginner counselorsHayashida, YumahaveUetsuji, TomoyadifficultyKoyamada, Koji doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2017
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    The beginner counselors have difficulty doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • category classification of text data with machine learning technique for visualizing flow of conversation in counseling
    2017 Nicograph International (NicoInt), 2017
    Co-Authors: Yuma Hayashida, Tomoya Uetsuji, Yasuo Ebara, Koji Koyamada
    Abstract:

    The beginner counselors have more likely to continue counseling in their own interest, they have a high tendency to make great use of the Closed-Ended Question in order to confirm the interpretation with the client. While expert counselors are instructing the counseling skill to beginner counselors, we consider that the reaction of a client for a beginner counselor's Question is important to visualize in an appropriate method. To respond the request, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation data is very low in the current system. To improve this problem, we have implemented on the category classification method of text data with SVM (Support Vector Machine) as machine learning technique to visualize the flow of conversation in counseling. In addition, we have compared and evaluated with results of the initial classification method of the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the results in the current system.

Tomoya Uetsuji - One of the best experts on this subject based on the ideXlab platform.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2018
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    TheEbara, Yasuo beginner counselorsHayashida, YumahaveUetsuji, TomoyadifficultyKoyamada, Koji doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
    Computational Science Intelligence and Applied Informatics, 2017
    Co-Authors: Yasuo Ebara, Tomoya Uetsuji, Yuma Hayashida, Koji Koyamada
    Abstract:

    The beginner counselors have difficulty doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency Closed-Ended Question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each Question by beginner counselor, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.

  • category classification of text data with machine learning technique for visualizing flow of conversation in counseling
    2017 Nicograph International (NicoInt), 2017
    Co-Authors: Yuma Hayashida, Tomoya Uetsuji, Yasuo Ebara, Koji Koyamada
    Abstract:

    The beginner counselors have more likely to continue counseling in their own interest, they have a high tendency to make great use of the Closed-Ended Question in order to confirm the interpretation with the client. While expert counselors are instructing the counseling skill to beginner counselors, we consider that the reaction of a client for a beginner counselor's Question is important to visualize in an appropriate method. To respond the request, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation data is very low in the current system. To improve this problem, we have implemented on the category classification method of text data with SVM (Support Vector Machine) as machine learning technique to visualize the flow of conversation in counseling. In addition, we have compared and evaluated with results of the initial classification method of the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the results in the current system.

W. Michael Hanemann - One of the best experts on this subject based on the ideXlab platform.

  • Valuing the Environment through Contingent Valuation
    Journal of Economic Perspectives, 1994
    Co-Authors: W. Michael Hanemann
    Abstract:

    Contingent valuation is now used around the world to value many types of public goods, including transportation, sanitation, health, and education, as well as the environment. The author describes how researchers go about making such surveys reliable, mentioning recent innovations in sampling, Questionnaire design, and data analysis, including formulating the valuation as a Closed-Ended Question about voting in a referendum to raise taxes for a particular purpose. He addresses various objections that contingent valuation results are incompatible with economic theory. Even without a market, there still exists a latent demand curve for nonmarket goods; contingent valuation represents a way to tease this out.