Fujitsu Corporation

Nobuo Watanabe

Kazuo Asakawa

Akira Kawamura

Ryusuke Masuoka

Takashi Kimoto

Jun'ichi Tanahashi

Hiroyuki Okada

Hiroshi Yamakawa

Masahiro Asaoka

Koji Maruhashi

Yoshio Nakao

Yuri Owada

Ryushuke Masuoka

Shigenori Matsuoka

An entrustee information transmitting method includes an entrustment managing storage storing entrustment relationship information indicating correspondence between entrusters who are voters performing entrustment and entrustees who are voters entrusted by the entrusters in an entrustment managing storage unit; an attendance managing storage storing attendance information of respective voters in an attendance managing storage unit in units of bills; an indirect-entrustment voter extracting operation of extracting one or more indirect-entrustment voter who indirectly entrust an attendee by using the entrustment relationship information and the attendance information; an entrustee determining operation of determining attendees who are indirectly entrusted by the indirect-entrustment voters to be recommended entrustees; and an entrustee information transmitting operation of transmitting information of the recommended entrustees.

Entrustee information transmitting apparatus, entrustee information transmitting method and computer readable medium having a program implementing same

The feature quantity selection vectors, indicating which features among those respecting discrete events are to be selected, are fixed. The feature quantity selection spaces taking discrete features as coordinate axes are divided into each segment, and the number of events contained therein is counted. The segments are arranged in order of diminishing event counts contained therein, and the values for functions evaluating the extent of relevance are scanned in the order of segments for which the segments are arranged from the maximum segment counts in the descending order. When the partial situations determined by selection segment counts that are maximum values take evaluation values as their maximum values, even where the feature quantity selection counts are changed, those partial situations are extracted.

Apparatus for data decomposition and method and storage medium therefor

A learning system is used in a data processing apparatus for learning an input pattern by obtaining an internal-state value necessary for realizing a desired data conversion by performing a pattern conversion defined by the internal-state value and calculating an output pattern corresponding to the input pattern. The learning system comprises a pattern presenting unit for presenting an input pattern group of the subject to be learned for pattern conversion, dividing the input pattern group of the subject to be learned into at least two sets, selecting one of the divided sets, presenting the input pattern group of the selected set to a pattern conversion unit and presenting an input pattern group belonging to all the sets presented up to the current point when the internal-state value to be converged is obtained in accordance with the presentation of the selected set, and an error value calculating unit for calculating an error value representing a magnitude of a non-consistency between an output pattern group outputted in accordance with the presentation and a teacher pattern group representing a pattern to be obtained by the output pattern group.

Learning system for a data processing apparatus

Neuro-fuzzy-integrated data processing system

A learning system is used in a data processing apparatus for learning an input pattern by obtaining an internal-state value necessary for realizing a desired data conversion by performing a pattern conversion defined by the internal-state value and calculating an output pattern corresponding to the input pattern. The learning system comprises a pattern presenting unit for presenting an input pattern group of the subject to be learned for pattern conversion, dividing the input pattern group of the subject to be learned into at least two sets, selecting one of the divided sets, presenting the input pattern group of the selected set to a pattern conversion unit and presenting an input pattern group belonging to all the sets presented up to the current point when the internal-state value to be converged is obtained in accordance with the presentation of the selected set, and an error value calculating unit for calculating an error value representing a magnitude of a non-consistency between an output pattern group outputted in accordance with the presentation and a teacher pattern group representing a pattern to be obtained by the said output pattern group.

A learning process system is provided for a neural network. The neural network is a layered network comprising an input layer, an intermediate layer and an output layer formed of basic units. In the basic units, a plurality of inputs is multiplied by a weight signal and the products are accumulated, thereby supplying the sum of products. An output signal is obtained using a threshold value function in response to the sum of products. An error signal is generated by an error circuit in response to a difference between the output signal obtained from the output layer and a teacher signal. A weight updating signal is determined in a weight learning circuit by obtaining a weight value in which the sum of the error values falls within an allowable range. Thus, the learning is performed in the layered neural network through use of a back propagation method. Through such learning in the layered neural network, an updating quantity to be obtained in the present weight updating cycle is determined in response to a once delayed weight updating quantity signal in a previous weight updating cycle and a twice delayed weight updating quantity obtained at a twice-previous weight updating cycle prior to the previous weight updating cycle.

Learning process system for use with a neural network structure data

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Zama, Japan

Nobuo Watanabe