Information processing apparatus, information processing method, pattern recognition apparatus, and pattern recognition method
First Claim
1. An information processing apparatus which includes a data input layer and an arithmetic processing layer having at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected and executes parallel hierarchical processing, said apparatus comprising:
- at least one processing module which has finished learning, which includes a plurality of neurons having a receptor field structure used to detect a predetermined feature class in the arithmetic processing layer of at least one predetermined layer level; and
at least one processing module which has not finished learning yet, which includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure, wherein learning necessary for detection of a new feature class is executed in said processing module which has not finished learning yet by presenting a predetermined pattern to the data input layer.
1 Assignment
0 Petitions
Accused Products
Abstract
In a hierarchical neural network having a module structure, learning necessary for detection of a new feature class is executed by a processing module which has not finished learning yet and includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure by presenting a predetermined pattern to a data input layer. Thus, a feature class necessary for subject recognition can be learned automatically and efficiently.
-
Citations
29 Claims
-
1. An information processing apparatus which includes a data input layer and an arithmetic processing layer having at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected and executes parallel hierarchical processing, said apparatus comprising:
-
at least one processing module which has finished learning, which includes a plurality of neurons having a receptor field structure used to detect a predetermined feature class in the arithmetic processing layer of at least one predetermined layer level; and
at least one processing module which has not finished learning yet, which includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure, wherein learning necessary for detection of a new feature class is executed in said processing module which has not finished learning yet by presenting a predetermined pattern to the data input layer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 19)
-
-
11. An information processing apparatus which includes a data input layer, an arithmetic processing layer having at least one layer level and a plurality of processing modules corresponding to feature classes to be detected, a learning control circuit, and a processing module addition/deletion control circuit, said apparatus comprising:
-
determination means for determining whether an error signal of an operation element output at a predetermined layer level in the arithmetic processing layer satisfies a predetermined condition in a predetermined learning step executed by the learning control circuit; and
control means for, when said determination means determines that the predetermined condition is satisfied, executing control to cause the processing module addition/deletion control circuit to add at least one processing module in a layer of a level lower than the layer level. - View Dependent Claims (12, 13, 14)
-
-
15. An information processing apparatus comprising:
-
an input layer which inputs predetermined input data;
independent component analysis means for executing independent component analysis for a predetermined data set;
learning control means;
an arithmetic processing layer which has at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected; and
learning data setting means for setting learning input data containing a predetermined feature class to be learned by a predetermined processing module from the input data and a predetermined base data set obtained as a result of independent component analysis and storing the learning input data in a predetermined memory, wherein said arithmetic processing layer of at least one predetermined layer level comprises at least one processing module which has finished learning, which includes a plurality of neurons having a receptor field structure used to detect a predetermined feature class, and at least one processing module which has not finished learning yet, which includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure, and said learning control means executes learning necessary for detection of a new feature class in said processing module which has not finished learning yet by presenting the predetermined learning input data to a predetermined layer of said arithmetic processing layer. - View Dependent Claims (16)
-
-
17. A pattern recognition apparatus comprising:
-
a data input layer;
an arithmetic processing layer which has at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected;
learning control means; and
recognition result output means for outputting a predetermined pattern recognition result on the basis of an output from said arithmetic processing layer, wherein said arithmetic processing layer of at least one predetermined layer level comprises at least one processing module which has finished learning, which includes a plurality of neurons having a receptor field structure used to detect a predetermined feature class, and at least one processing module which has not finished learning yet, which includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure, and said learning control means executes learning necessary for detection of a new feature class in said processing module which has not finished learning yet by presenting a predetermined pattern to said data input layer. - View Dependent Claims (20)
-
-
18. A pattern recognition apparatus comprising:
-
a data input layer;
an arithmetic processing layer which has at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected;
a learning control circuit;
a processing module addition/deletion control circuit; and
recognition result output means for outputting a predetermined pattern recognition result on the basis of an output from said arithmetic processing layer, wherein when an error signal of an operation element output at a predetermined layer level in said arithmetic processing layer satisfies a predetermined condition in a predetermined learning step executed by said learning control circuit, said processing module addition/deletion control circuit adds at least one processing module in a layer of a level lower than the layer level.
-
-
21. An information processing method executed by an information processing apparatus which includes a data input layer, an arithmetic processing layer having at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected, at least one processing module which has finished learning, which includes a plurality of neurons having a receptor field structure used to detect a predetermined feature class in the arithmetic processing layer of at least one predetermined layer level, and at least one processing module which has not finished learning yet, which includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure,
wherein learning necessary for detection of a new feature class is executed in the processing module which has not finished learning yet by presenting a predetermined pattern to the data input layer.
-
22. An information processing method executed by an information processing apparatus which includes a data input layer, an arithmetic processing layer having at least one layer level and a plurality of processing modules corresponding to feature classes to be detected, a learning control circuit, and a processing module addition/deletion control circuit, said method comprising:
-
a determination step of determining whether an error signal of an operation element output at a predetermined layer level in the arithmetic processing layer satisfies a predetermined condition in a predetermined learning step executed by the learning control circuit; and
a control step of, when it is determined in the determination step that the predetermined condition is satisfied, executing control to cause the processing module addition/deletion control circuit to add at least one processing module in a layer of a level lower than the layer level.
-
-
23. An information processing method executed by an information processing apparatus which includes
an input layer which inputs predetermined input data, independent component analysis means for executing independent component analysis for a predetermined data set, learning control means, an arithmetic processing layer which has at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected, and learning data setting means for setting learning input data containing a predetermined feature class to be learned by a predetermined processing module from the input data and a predetermined base data set obtained as a result of independent component analysis and storing the learning input data in a predetermined memory, the arithmetic processing layer of at least one predetermined layer level including at least one processing module which has finished learning, which includes a plurality of neurons having a receptor field structure used to detect a predetermined feature class, and at least one processing module which has not finished learning yet, which includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure, wherein said learning control means executes learning necessary for detection of a new feature class in the processing module which has not finished learning yet by presenting the predetermined learning input data to a predetermined layer of the arithmetic processing layer.
-
24. A pattern recognition method executed by a pattern recognition apparatus including
a data input layer, an arithmetic processing layer which has at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected, learning control means, and recognition result output means for outputting a predetermined pattern recognition result on the basis of an output from the arithmetic processing layer, the arithmetic processing layer of at least one predetermined layer level including at least one processing module which has finished learning, which includes a plurality of neurons having a receptor field structure used to detect a predetermined feature class, and at least one processing module which has not finished learning yet, which includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure, wherein said learning control means executes learning necessary for detection of a new feature class in the processing module which has not finished learning yet by presenting a predetermined pattern to the data input layer.
-
25. A pattern recognition method executed by a pattern recognition apparatus including
a data input layer, an arithmetic processing layer which has at least one layer level and a plurality of processing modules corresponding to a plurality of feature classes to be detected, a learning control circuit, a processing module addition/deletion control circuit, and recognition result output means for outputting a predetermined pattern recognition result on the basis of an output from the arithmetic processing layer, wherein when an error signal of an operation element output at a predetermined layer level in the arithmetic processing layer satisfies a predetermined condition in a predetermined learning step executed by the learning control circuit, the processing module addition/deletion control circuit adds at least one processing module in a layer of a level lower than the layer level.
-
28. An information processing apparatus which executes processing of correcting a connection constant between layers to set, as a desired output value, an output value from each neuron included in a predetermined processing module in a predetermined detection layer in a hierarchical neural network which is constituted by alternately arranging, between a data input layer and a data output layer, a detection layer which includes a plurality of processing modules to detect a feature amount to be detected from an output from a layer of a preceding stage and an integration layer which integrates and outputs outputs from the detection layer, said apparatus comprising:
-
error calculation means for obtaining an error between the desired output value and the output value from each neuron included in the predetermined processing module in the predetermined detection layer;
addition means for adding a new processing module in accordance with the error in at least one layer of the layers arranged between the data input layer and the layer which outputs the output value to the predetermined processing module; and
correction means for, after addition processing by said addition means, correcting the connection constant between a predetermined number of layers from the predetermined detection layer to the data input layer on the basis of the error by said error calculation means.
-
-
29. An information processing method of executing processing of correcting a connection constant between layers to set, as a desired output value, an output value from each neuron included in a predetermined processing module in a predetermined detection layer in a hierarchical neural network which is constituted by alternately arranging, between a data input layer and a data output layer, a detection layer which includes a plurality of processing modules to detect a feature amount to be detected from an output from a layer of a preceding stage and an integration layer which integrates and outputs outputs from the detection layer, said method comprising:
-
an error calculation step of obtaining an error between the desired output value and the output value from each neuron included in the predetermined processing module in the predetermined detection layer;
an addition step of adding a new processing module in accordance with the error in at least one layer of the layers arranged between the data input layer and the layer which outputs the output value to the predetermined processing module; and
a correction step of, after addition processing in the addition step, correcting the connection constant between a predetermined number of layers from the predetermined detection layer to the data input layer on the basis of the error in the error calculation step.
-
Specification