the method or algorithm described in connection with the examples disclosed herein may be embodied directly in hardware , in a software module executed by a processor , or in a combination of the two . a software module may reside in ram memory , flash memory , rom memory , eprom memory , eeprom memory , registers , hard disk , a removable disk , a cd - rom , or any other form of storage medium known in the art . an exemplary storage medium is coupled to the processor such that the processor can read information from , and write information to , the storage medium . in the alternative , the storage medium may be integral to the processor . the processor and the storage medium may reside in an application specific integrated circuit ( asic ). the processor may be any processing device such as a controller , a microprocessor , a software and / or hardware module , etc . fig1 shows a block diagram of a training view of a fuzzy name search apparatus 100 according to an embodiment of the present invention . a name or phrase 102 may be entered or input into a computer ( e . g ., a desktop , pda , cell phone , software and / or hardware module , etc .) that performs a rule driven search 104 . the rule driven search 104 may search databases ( e . g ., third party databases or local databases ), the internet , etc . for information related to the name or phrase 102 . a search engine 106 using fuzzy name search rules identifies fuzzy matches 108 . the fuzzy matches 108 are passed through a rule filter 110 that filters the fuzzy matches 108 by applying learned rules 112 . the rule filter 110 outputs one or more matches 114 that are displayed on a name match display 116 for a user to make match selections . the user may use a touch pad , a touch screen or a keyboard to make the match selections . the selected matches 120 are input into a report generator 124 . the report generator 124 generates appropriate reports 126 . for example , the user may wish to search a database for encumbrances on a property at 1135a greenacre drive . the user may enter the name or address 102 as 1135 greenacre drive into a computer to perform a rule driven search 104 . the search engine 106 may return a number of fuzzy matches such as 1135a green acre drive , 1125a green acre drive , 1135 greenacre cove , 1135b greenacre , etc . the rule filter 110 would then filter the names using learned rules 112 . the learned rules 112 may include a number of rules such as reject all addresses having less than a perfect match for a street number . in the example , the address 1125a green acre drive would be filtered out by the rule filter 110 because it does not have a perfect match for the street number . the matches 114 may be displayed on the name match display 116 for the user to select the matches 114 the user desires . the user may , for example , realize that greenacre and green acre are alternate spellings of the same street . the user may therefore select the match 1135b greenacre among other matches the user finds relevant . fig2 shows a block diagram of a learning view of a fuzzy name search apparatus 200 according to an embodiment of the present invention . a name or phrase 202 is entered or input into a computer that performs a rule driven search 104 . the rule driven search 104 may search databases ( e . g ., third party databases or local databases ), the internet , etc . for information related to the name or phrase 202 . the rule driven search 104 results in matches 114 that are displayed on a name match display 116 for a user to make match selections . the name match display 116 displays all fuzzy logic matches . the learned rules 112 ( shown in fig1 as being a part of the rule driven search 104 ) may be applied with any proposed filtered matches displayed to the user as “ stricken ” matches . the user may accept or reject any of the fuzzy logic matches including the “ stricken ” matches . the user selections 118 are forwarded as selected matches 120 to a training set repository 204 . the training set repository 204 may include a training set database 206 , which stores the selected matches . the training set repository 204 may also include a training engine 208 that receives candidate samples 210 from the training set database 206 . the training engine 208 identifies the candidate samples 210 that should be filtered from the training set database 206 . the candidate samples 210 , identified to be filtered , are filtered using a filter 212 . the training set repository 204 also feeds a rule search generator 214 that modifies the search rules for subsequent rule driven searches 104 and sample management functions ( explained hereinafter ) of the training set repository 204 . the learning view of the fuzzy name search apparatus 200 has at least two components that distinguish it from the training view of the fuzzy name search apparatus 100 . the first component is the training set repository 204 . the training set repository 204 has a training set database 206 that stores user choices . the training set database 206 provides the information needed to allow the fuzzy name search apparatus 200 to learn . the second component is the rule search generator 214 . the rule search generator 214 uses the training set database 206 to modify the fuzzy search rules based on user selections . the rule search generator 214 modifies the rules based on user selections . the rules are updated for future searches and to select historic samples for elimination . fig3 shows a block diagram of the training set repository 204 of fig2 according to an embodiment of the present invention . the selected matches 120 are input into a new samples module 302 of the training set database 206 . the new samples module 302 of the training set database 206 sends the selected matches 120 to a historic samples module 304 of the training set database 206 . at various times , m samples 306 from the new samples module 302 and n samples 308 from the historic samples module 304 are selected as candidate samples 310 for elimination from the training set database 206 . the candidate samples 310 are partitioned into y partitions 312 that are inputs to the training engine 208 . the training engine 208 receives search rules 314 from the rule search generator 214 . the search rules 314 are replicated y times . the candidate sample partitions 312 are further partitioned into training samples that are used to train respective rule sets r 1 to r y 314 using machine learning algorithms rg 1 to rg y 313 and testing samples that are inputs to respective test databases 316 . the training engine 208 applies cross validation techniques to estimate the accuracy of the learning for each candidate sample partition 312 . the candidate sample partition 312 with the most accurate learning is retained as the new training set . for example , if the training set contains 11 , 000 samples , 10 subsets of 10 , 000 samples each are generated , training and cross validation is performed using each partition , and the accuracy is calculated for each of the 10 partitions . the 10 , 000 samples of the partition with best accuracy are retained as the new training set , thus reducing the total number of samples from 11 , 000 down to 10 , 000 . the training engine 208 uses the test samples to generate accuracy indices a 1 to a y 318 . the accuracy indices 318 are used by the training set repository 204 to filter samples 212 from the historic samples module 304 of the training set database 206 . fig4 shows a flow diagram of a method of searching a database using fuzzy rules according to an embodiment of the present invention . first , a name search request is received for a fuzzy name search ( 402 ). the name may be a person &# 39 ; s name , an address , a zip code , any combination thereof , or any word or word phrase for searching a database . the name may also include search attributes for searching a relational database . user attributes , application attributes or any other indicia on which machine learning may be accomplished may also be received . the name or other data may be received in a digital computer or equivalent . rules may also be presented to the user for selection . the rules may , for example , have rules that return all matches having common nicknames for a given name . a rule driven search may also be performed ( 404 ). learned rules are applied and items regarded as potential mismatches are “ stricken ” ( 405 ). the matches may be displayed on a display screen ( 406 ). for example , the search may have been on richard james and the fuzzy results show records for equivalent names such as ricky james , rick james , rich james , james richard , james richards , jimmy richards , jimmy rick , etc . some of the matches may be stricken based on application of learned rules . the user may then select the desired matches “ stricken ” or not ( 408 ). for example , the user may decide that records for ricky james , rick james , rich james , and james richard are likely to be relevant while those for james richards , jimmy richards and jimmy rick are likely to be irrelevant . a search report may then be generated ( 410 ). the search reports may be tailored to a particular industry such as the title search industry . the search reports may , for example , contain chain of title information or recorded land encumbrances for each relevant record . the training set database 206 may be updated with the user selections ( 412 ). the search rules may then be updated ( 414 ) according to the user selections and historic user selections stored in the training set database 206 . for example , it may be noted that for virtually all of the samples in the training set database 206 , the user has selected all equivalent names : 1 ) where a nickname associated with the given name is used with the correct last name and 2 ) where the first name and the last name have been transposed have been selected ; but the user has rejected all equivalent names with a nickname and the last name where the nickname and the equivalent name have been transposed . in this case , the system may learn a new rule or set of rules . fig5 shows a flow diagram of a method of filtering or refreshing a fuzzy search training set repository according to an embodiment of the present invention . matches and user selections are received ( 502 ). the matches and user selections may be used to update a sample database ( 504 ). the database may store search samples , user selections , and any applicable attributes of the fuzzy matches or the search . the sample database may contain historic as well as new samples . the candidate samples 310 may be partitioned into y sets of candidate samples ( 508 ). each of the y sets may contain the target maximum number of samples configured for the system . each sample set may be further partitioned or divided into training and testing samples ( 510 ). a set of y test rule sets may be created . y copies of the rule set may be generated . each of the rule sets may be trained using respective training samples . the training engine 208 may generate y trained rule sets using the y training samples ( 512 ). the accuracy of each trained rule - set may be tested using k - fold accuracy validation or cross - validation ( 514 ). the sample database may then be replaced with samples from the partition having the highest accuracy ( 516 ). the description of the disclosed examples is provided to enable any person of ordinary skill in the art to make and use the disclosed methods and apparatus . various modifications to these examples will be readily apparent to those skilled in the art , and the principles defined herein may be applied to other examples without departing from the spirit and scope of the disclosed method and apparatus . the described embodiments are to be considered in all respects only as illustrative and not restrictive and the scope of the present invention is , therefore , indicated by the appended claims rather than by the foregoing description . all changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope .