Data classifier using learning-formed and clustered map
AssigneeFuji Xerox Co., Ltd.
InventorsHitoshi Ikeda, Noriji Kato, Hirotsugu Kashimura
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Abstract
A data classifier performs a data classification process using prototypes classified into clusters. A prototype map is formed using mapping means and clustering means. The mapping means forms, through learning, a prototype map by adjusting coupling weights between a plurality of prototypes provided in a map space based on a plurality of input data. The clustering means calculates a predetermined measure between the prototypes and classifies the prototypes into a plurality of clusters based on the measure.
Claims24
A data classifier for classifying input data using a result of a learning process based on the input data and using a self-organizing map, the classifier comprising:
- means for forming, through learning, a plurality of self-organizing maps corresponding to a plurality of learning parameters;
- and
- means for evaluating each of the plurality of learning-formed self-organizing maps and adjusting the learning parameters based on the result of the evaluation, wherein
- a learning formation process of a self-organizing map is again performed using the adjusted learning parameters.
A data classifier comprising:
- mapping means for adjusting coupling weights between a plurality of prototypes in a map space using a plurality of input data and forming a prototype map through learning;
- and
- clustering means for calculating a predetermined measure defined between each prototype and classifying the prototypes into a plurality of clusters based on the measure, wherein
- the prototype map classified into clusters is supplied for a data classification process.
A method for classifying data by executing a learning process based on input data using a self-organizing map and classifying data using the learning result, the method comprising the steps of:
- forming, through learning, a plurality of self-organizing maps corresponding to a plurality of learning parameters;
- and
- evaluating each of the plurality of learning-formed self-organizing maps and adjusting the learning parameters based on the evaluation result, wherein
- a self-organizing map is again formed through learning with the adjusted learning parameters.
A method for classifying data, comprising:
- a mapping step for forming, through learning, a prototype map by adjusting coupling weights between a plurality of prototypes in a map space according to a plurality of data inputs, and
- a clustering step for calculating a predetermined measure defined between each prototype and classifying the prototypes into a plurality of clusters based on the measure, wherein
- the prototype map classified into clusters is supplied for a data classification process.
A data classifying program which instructs a computer to execute a process to perform learning based on input data using a self-organizing map and to classify data using the results of the learning, the program instructing a computer to execute the steps of:
- forming, through learning, a plurality of self-organizing maps corresponding to a plurality of learning parameters;
- and
- evaluating each of the plurality of learning-formed self-organizing maps and adjusting the learning parameters based on the evaluation results, wherein
- a learning formation process for a self-organizing map is again performed with the adjusted learning parameters.
A data classifying program which instructs a computer to execute the steps of:
- a mapping procedure for adjusting coupling weights between a plurality of prototypes in a map space using a plurality of input data and for forming, through learning, a prototype map, and
- a clustering procedure for calculating a predetermined measure defined between each prototype and for classifying the prototypes into a plurality of clusters based on the measure, wherein
- the prototype map classified into clusters is supplied for a data classification process.
Dependencies are inferred from the claim text.