Method and computer-based sytem for non-probabilistic hypothesis generation and verification
First Claim
1. A computer-operable method for data processing involved in but not limited to image, pattern and sequence recognition, decision-making and machine-learning, comprising the steps of:
- (a) generation of a hypothesis-parameter, in addition to parameters already existing in a database, both for a reference object (or reference objects) to be used as benchmark(s) in data processing, and for all other objects in a database that are subjects of a comparative analysis (hereunder referred to as target objects);
(b) assigning of digital values to reference objects in a hypothesis-parameter, such digital values being a reflection of a certain hypothesis of a relationship between said reference objects—
based on either an a priori existing idea or a result of a preliminary experimental study, including clustering, of objects covered by said hypothesis-parameter;
(c) assigning of certain digital values to all target objects in a hypothesis-parameter;
(d) using a hypothesis-parameter in clustering of objects, along with plurality of other parameters describing objects under clustering, (e) establishing a number of copies (hereunder referred to as multiplication number) of hypothesis-parameter required for compensation, during a clustering process, of effect of all other parameters describing a given object so that clustering based on thus established number of copies of a hypothesis-parameter along with the rest of parameters is identical to clustering produced upon use of a hypothesis-parameter as the only parameter, (f) consecutive addition of each target object to a reference object (or reference objects); and
(g) using an established multiplication number for measurement of dissimilarity between reference object(s) and target objects, hence verification of validity of a hypothesis underlying a generated hypothesis-parameter.
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Abstract
The invention provides a method, apparatus and algorithm for data processing that allows for hypothesis generation and the quantitative evaluation of its validity. The core procedure of the method is the construction of a hypothesis-parameter, acting as an “ego” of the non-biological reasoning system. A hypothesis-parameter may be generated either based on totality of general knowledge facts as a global description of data, or by a specially designed “encapsulation” technique providing for generation of hypothesis-parameters in unsupervised automated mode, after which a hypothesis-parameter is examined for the concordance with a totality of parameters describing objects under analysis. The hypothesis examination (verification) is done by establishing a number of copies of a hypothesis-parameter that may adequately compensate for the rest of existing parameters so that the clustering could rely on a suggested hypothesis-parameter. The method of this invention is based on the principle of the information thyristor and represents its practical implementation.
This invention can be used as a universal computer-based recognition system in robotic vision, intelligent decision-making and machine-learning.
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Citations
14 Claims
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1. A computer-operable method for data processing involved in but not limited to image, pattern and sequence recognition, decision-making and machine-learning, comprising the steps of:
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(a) generation of a hypothesis-parameter, in addition to parameters already existing in a database, both for a reference object (or reference objects) to be used as benchmark(s) in data processing, and for all other objects in a database that are subjects of a comparative analysis (hereunder referred to as target objects);
(b) assigning of digital values to reference objects in a hypothesis-parameter, such digital values being a reflection of a certain hypothesis of a relationship between said reference objects—
based on either an a priori existing idea or a result of a preliminary experimental study, including clustering, of objects covered by said hypothesis-parameter;
(c) assigning of certain digital values to all target objects in a hypothesis-parameter;
(d) using a hypothesis-parameter in clustering of objects, along with plurality of other parameters describing objects under clustering, (e) establishing a number of copies (hereunder referred to as multiplication number) of hypothesis-parameter required for compensation, during a clustering process, of effect of all other parameters describing a given object so that clustering based on thus established number of copies of a hypothesis-parameter along with the rest of parameters is identical to clustering produced upon use of a hypothesis-parameter as the only parameter, (f) consecutive addition of each target object to a reference object (or reference objects); and
(g) using an established multiplication number for measurement of dissimilarity between reference object(s) and target objects, hence verification of validity of a hypothesis underlying a generated hypothesis-parameter. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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- 13. The method of 8, wherein, in order to create artificial, or non-biological, intelligence, information processing is performed by processors of the information thyristor type wherein information itself serves as an information valve.
Specification