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Optimal Product Approximation Method of Probability Distribution by K-Kernel Dependency and Multiple Decision Combining Method by Dependency
Optimal Product Approximation Method of Probability Distribution by K-Kernel Dependency and Multiple Decision Combining Method by Dependency
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机译：K核依赖性的概率分布最优乘积逼近法和相依性多决策组合法
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摘要
An object of the present invention is to provide an optimal product approximation method of a probability distribution due to a higher order dependence of a first order or higher order.;It is another object of the present invention to provide a method for obtaining a constitutive distribution term in an optimal product approximation of a probability distribution due to a higher order dependency.;It is still another object of the present invention to provide a method of combining multiple decisions by dependency.;In order to achieve the first object, The optimal product approximation method of the probability distribution due to the differential dependence,;K set of determiners ,;A set of L decision candidates ,;Input ,;The degree of dependency is ,; If it is,;Probability of force terrier end; = when,;= Lt; / RTI &; end Lt; / RTI ; If it is,; The And;= If the relationship is established,;The above-mentioned high-order probability distribution To In a method of optimally approximating a product of a low-order probability distribution by a differential dependence,;Obtaining a primary dependency relationship;;Obtaining a conditionally independent assumption;;Obtaining a secondary dependency relationship;;Obtaining a conditional primary dependency;;.;.;.; Obtaining a differential dependency; And;Conditional And a step of obtaining a differential dependence relationship.;In order to achieve the above-mentioned second object, The method of obtaining the constituent distribution term at the time of the optimal product approximation of the probability distribution by the differential dependence is as follows:;K set of determiners ,;A set of L decision candidates ,;Input ,;The degree of dependency is ,; ,;Probability of force terrier end; = when,;= Lt; / RTI &; end Lt; / RTI ; when,; The And;= If the relationship is established,; A method for finding a constituent distribution term at an optimal product approximation of a probability distribution by a differential dependence,;Actual probability distribution , An approximate probability distribution when,; Defined as;Minimize the measure of closeness Quot;;for ㉠ do / * Primary dependency * /;/ * Here, the above is the first dependency relation If; And; Given as * /;for ㉡ do / * Secondary dependency * /;/ * Where the above is the second dependency relation If; And; Given as * /;........ ........;for ㉢ do / * ( -1) Dependency relation * /;/ * At this time, -1) As a dependency relation 0 i ( -1) (j), ..., i1 (j) j; And; Given as * /;while (㉣) do / * Car dependency relationship * /;/ * / RTI 0 as a car dependency i (j), ..., i1 (j) j; And; Given as * /;.......;end;end;.......;end;end;, One primary dependency, one secondary dependency, ..., one ( -1) dependency, (K- )doggy And the dependency relation -1) nested for loops and one while loop.;According to another aspect of the present invention,;K set of determiners ,;A set of L decision candidates ,;Input ,;The degree of dependency is ,; When there is a relationship, A method for combining a decision method by performing a method of obtaining an optimal product approximation of a probability distribution due to a differential dependence and a constituent distribution term for this with a normal Bayesian decision method,;Referring to the training sample data, A first step of obtaining an optimal product approximate set of a probability distribution by a differential dependence; And;A second step of probabilistically combining the determinations of the plurality of determinants by applying the approximate set obtained in the above step to the combination formula with the Bayesian method;And is characterized by considering dependencies between determinants that make multiple decisions without requiring an independent assumption.;The application fields and effects of the present invention are as follows:;1) The present invention avoids the problems that can be caused by independent households by not taking an independent assumption.;2) In approximating the higher-order probability distribution by the product of the lower-order probability distributions, it is possible to obtain the optimal product approximate distribution set based on the order dependence relation while changing the dependency order.;3) The required storage complexity is larger than that of the independent family, but it is smaller than the BKS method. Because O (L 2 ) O (L k + 1 ) O (L K + 1 ).;4) Extending existing research results on product approximation by suggesting a methodology that can deal with higher order dependency, not just the first dependency relation.;5) It has been shown that combining the decisions of multiple determinants based on higher order dependence is superior in performance.;6) In the field of pattern recognition, the present inventors have excelled in the present invention through experiments in which a plurality of recognizers are used to combine the determinations.;7) In the field of pattern recognition, the excellence of the present invention is shown through experiments combining a plurality of recognizers with the determinations. Although the present invention has been described with respect to only the embodiment of the pattern recognition field, the idea of the present invention is applicable to other fields such as group decision making and control fields.
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