View source for 18265346. LEARNING APPARATUS, LEARNING METHOD, ANOMALY DETECTION APPARATUS, ANOMALY DETECTION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM simplified abstract (NEC Corporation)

=LEARNING APPARATUS, LEARNING METHOD, ANOMALY DETECTION APPARATUS, ANOMALY DETECTION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM=


==Organization Name==

[[:Category:NEC Corporation|NEC Corporation]]


[[Category:NEC Corporation]]

==Inventor(s)==

[[:Category:Shohei Mitani of Tokyo (JP)|Shohei Mitani of Tokyo (JP)]][[Category:Shohei Mitani of Tokyo (JP)]]

[[:Category:Naoki Yoshinaga of Tokyo (JP)|Naoki Yoshinaga of Tokyo (JP)]][[Category:Naoki Yoshinaga of Tokyo (JP)]]

==LEARNING APPARATUS, LEARNING METHOD, ANOMALY DETECTION APPARATUS, ANOMALY DETECTION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18265346 titled 'LEARNING APPARATUS, LEARNING METHOD, ANOMALY DETECTION APPARATUS, ANOMALY DETECTION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM

==Simplified Explanation==

The learning apparatus described in the patent application includes a learning unit that learns two parameters, a first parameter and a second parameter, which are part of a mapping model. The mapping model is used to map a region set based on a pre-defined subspace set and the distance from the subspace. The learning unit generates a feature vector based on normal data input as training data, where the first parameter is used for generating the feature vector and the second parameter is used for adjusting the distance.

* The learning apparatus includes a learning unit that learns two parameters for a mapping model.
* The mapping model is used to map a region set based on a pre-defined subspace set and the distance from the subspace.
* The learning unit generates a feature vector based on normal data input as training data.
* The first parameter is used for generating the feature vector.
* The second parameter is used for adjusting the distance.

Potential applications of this technology:

* Machine learning and artificial intelligence systems
* Pattern recognition and classification systems
* Data analysis and modeling
* Image and speech processing
* Robotics and autonomous systems

Problems solved by this technology:

* Efficient mapping of a region set based on a pre-defined subspace set and distance
* Accurate generation of feature vectors from normal data input
* Adjusting the distance from the subspace for improved mapping accuracy

Benefits of this technology:

* Improved accuracy and efficiency in mapping and classification tasks
* Enhanced performance in pattern recognition and data analysis
* Increased adaptability and flexibility in machine learning systems
* Potential for improved decision-making and problem-solving capabilities in various domains


==Original Abstract Submitted==

A learning apparatus includes: a learning unit that learns a first parameter and a second parameter that are included in a mapping model for mapping, to a region set based on a subspace set in advance and a distance from the subspace, a feature vector generated based on normal data input as training data, the first parameter being for generating the feature vector and the second parameter being for adjusting the distance.



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