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memory - based learning of classification tasks is a branch of supervised machine learning in which the learning phase consists simply of storing all encountered instances from a training set in memory @xcite .
memory - based learning algorithms do not invest effort during learning in abstracting from the training data , such as eager - learning ( e.g. , decision - tree algorithms , rule - induction , or connectionist - learning algorithms , @xcite ) do .
rather , they defer investing effort until new instances are presented .
on being presented with an instance , a memory - based learning algorithm searches for a best - matching instance , or , more generically , a set of the @xmath0 best - matching instances in memory .
having found such a set of @xmath0 best - matching instances , the algorithm takes the ( majority ) class with which the instances in the set are labeled to be the class of the new instance .
pure memory - based learning algorithms implement the classic @xmath0-nearest neighbour algorithm @xcite ; in different contexts , memory - based learning algorithms have also been named lazy , instance - based , exemplar - based , memory - based , case - based learning or reasoning @xcite )    memory - based learning has been demonstrated to yield accurate models of various natural language tasks such as grapheme - phoneme conversion , word stress assignment , part - of - speech tagging , and pp - attachment @xcite .
for example , the memory - based learning algorithm ib1-ig @xcite , which extends the well - known ib1 algorithm @xcite with an information - gain weighted similarity metric , has been demonstrated to perform adequately and , moreover , consistently and significantly better than _ eager - learning _ algorithms which do invest effort in abstraction during learning ( e.g. , decision - tree learning @xcite , and connectionist learning @xcite ) when trained and tested on a range of morpho - phonological tasks ( e.g. , morphological segmentation , grapheme - phoneme conversion , syllabification , and word stress assignment ) @xcite .
thus , when learning nlp tasks , the abstraction occurring in decision trees ( i.e. , the explicit _ forgetting _ of information considered to be redundant ) and in connectionist networks ( i.e. , a non - symbolic encoding and decoding in relatively small numbers of connection weights ) both hamper accurate generalisation of the learned knowledge to new material .
these findings appear to contrast with the general assumption behind eager learning , that data representing real - world classification tasks tends to contains ( i ) redundancy and ( ii ) exceptions : redundant data can be removed , yielding smaller descriptions of the original data ; some exceptions ( e.g. , low - frequency exceptions ) can ( or should ) be discarded since they are expected to be bad predictors for classifying new ( test ) material .
however , both redundancy and exceptionality can not be computed trivially ; heuristic functions are generally used to estimate them ( e.g. , functions from information theory @xcite ) .
the lower generalisation accuracies of both decision - tree and connectionist learning , compared to memory - based learning , on the above - mentioned nlp tasks , suggest that these heuristic estimates may not hold for data representing nlp tasks .
it appears that in order to learn such tasks successfully , a learning algorithm should not forget ( i.e. , explicitly remove from memory ) any information contained in the learning material : it should not abstract from the individual instances
.    an obvious type of abstraction that is not harmful for generalisation accuracy ( but that is not always acknowledged in implementations of memory - based learning ) is be the straightforward abstraction from tokens to types with frequency information .
in general , data sets representing natural language tasks , when large enough , tend to contain considerable numbers of duplicate sequences mapping to the same output or class .
for example , in data representing word pronunciations , some sequences of letters , such as ing at the end of english words , occur hundreds of times , while each of the sequences is pronounced identically , viz .
/i/. instead of storing all individual sequence tokens in memory , each set of identical tokens can be safely stored in memory as a single sequence type with frequency information , without loss of generalisation accuracy @xcite .
thus , forgetting instance tokens and replacing them by instance types may lead to considerable computational optimisations of memory - based learning , since the memory that needs to be searched may become considerably smaller .
given the safe , performance - preserving optimisation of replacing sets of instance tokens by instance types with frequency information , a next step of investigation into optimising memory - based learning is to measure the effects of _ forgetting instance types _ on grounds of their exceptionality , the underlying idea being that the more exceptional a task instance type is , the more likely it is that it is a bad predictor for new instances .
thus , exceptionality should in some way express the unsuitability of a task instance type to be a best match ( nearest neighbour ) to new instances : it would be unwise to copy its associated classification to best - matching new instances . in this paper
, we investigate three criteria for estimating an instance type s exceptionality , and removing instance types estimated to be the most exceptional by each of these criteria .
the criteria investigated are    1 .
typicality of instance types ; 2 .   class prediction strength of instance types ; 3 .
friendly - neighbourhood size of instance types ; 4 .
random ( to provide a baseline experiment ) .
we base our experiments on a large data set of english word pronunciation .
we briefly describe this data set , and the way it is converted into an instance base fit for memory - based learning , in section  [ data ] . in section  [ experimental ] we describe the settings of our experiments and the memory - based learning algorithm ib1-ig with which the experiments are performed .
we then turn to describing the notions of typicality , class - prediction strength , and friendly - neighbourhood size , and the functions to estimate them , in section  [ criteria ] .
section  [ results ] provides the experimental results . in section  [ discussion ] , we discuss the obtained results and formulate our conclusions .
converting written words to stressed phonemic transcription , i.e. , word pronunciation , is a well - known benchmark task in machine learning @xcite .
we define the task as the conversion of fixed - sized instances representing parts of words to a class representing the phoneme and the stress marker of the instance s middle letter . to generate the instances ,
windowing is used @xcite .
table  [ overall - windows - ex ] displays example instances and their classifications generated on the basis of the sample word booking .
classifications , i.e. , phonemes with stress markers ( henceforth pss ) , are denoted by composite labels .
for example , the first instance in table  [ overall - windows - ex ] , _ _ _ book , maps to class label /b/1 , denoting a /b/ which is the first phoneme of a syllable receiving primary stress .
in this study , we chose a fixed window width of seven letters , which offers sufficient context information for adequate performance , though extension of the window decreases ambiguity within the data set @xcite .    [ cols="^,^,^,^,^,^,^,^,^ " , ]
figure  [ edited - gs ] displays the generalisation accuracies in terms of incorrectly classified test instances obtained with all performed experiments . the leftmost point in the figure ,
from which all lines originate , indicates the performance of ib1-ig when trained on the full data set of 222,601 types , viz .
6.42% incorrectly classified test instances ( when computed in terms of incorrectly pronounced test words , ib1-ig pronounces 64.61 of all test words flawlessly ) .
the line graph representing the four experiments in which instance types are removed randomly can be seen as the baseline graph .
it can be expected that removing instances randomly leads to a degradation of generalisation performance .
the upward curve of the line graph denoting the experiments with random selection indeed shows degrading performance with increasing numbers of left - out instance types .
the relative decrease in generalisation accuracy is 2.0% when 1% of the training material is removed randomly , 3.8% with 2% random removal , 10.7% with 5% random removal , and 20.7% with 10% random removal .
=    surprisingly , the only experiments showing lower performance degradation than removal by random selection are those with class - prediction strength ; the other criteria for removing exceptional instances lead to worse degradations . it does not matter whether instance types are removed on grounds of their typicality : apparently , a markedly low , neutral , or high typicality value indicates that the instance type is ( on average ) important , rather than removable .
the same applies to friendly - neighbourhood size : instances with small neighbourhood sizes appear to contribute significantly to performance on test material .
it is remarkable that the largest errors with 1% and 2% removal are obtained with the friendly - neighbourhood size criterion : it appears that on average , the instances with few or no nearest neighbours are important in the classification of test material .
when using class - prediction strength as removal criterion , performance does not degrade until about 5% of the instance types with the lowest strength are removed from memory .
the reason is that class - prediction strength is the only criterion that detects minority ambiguities , i.e. , instance types with prediction strength 0.0 , that can not contribute to classification since they are always overshadowed by their counterpart instance types with majority classes , even for their own classification . in the training
set , 9,443 instance types are minority ambiguities , i.e. , 4.2% of the instance types ( accounting for 3.8% of the instance tokens in the original token set ) .
thus , among the tested methods for reducing the memory needed for storing an instance base in memory - based learning , only two are performance - preserving while accounting for a substantial reduction in the amount of memory needed by ib1-ig :    1 .
replacing instance tokens by instance types accounts for a reduction of about 63% of memory needed to store instances , excluding the memory needed to store frequency information .
when frequency information is stored in two bytes per instance type , the memory reduction is about 54% .
removing instance types that are minority ambiguities on top of the type / token - reduction accounts only for an additional memory reduction of 2% , i.e. , for a total memory reduction of 65% ; 56% with two - byte frequency information stored per instance .
as previous research has suggested @xcite , keeping full memory in memory - based learning of word pronunciation strongly appears to yield optimal generalisation accuracy .
the experiments in this paper show that optimisation of memory use in memory - based learning while preserving generalisation accuracy can only be performed by ( i ) replacing instance tokens by instance types with frequency information , and ( ii ) removing minority ambiguities .
both optimisations can be performed straightforwardly ; minority ambiguities can be traced with less effort than by using class - prediction strength .
our implementation of ib1-ig described in @xcite already makes use of this knowledge , albeit partially ( it stores class distributions with letter - window types ) .
we note that with @xmath1 ( in our tested implementation of ib1-ig , @xmath2 ) , removing minority ambiguities may distort performance , since taking more than single nearest neighbours into account may allow for minority ambiguities to play a constructive role in classification .
our results also show that atypicality , non - typicality , and typicality @xcite , and friendly - neighbourhood size are all estimates of exceptionality that indicate the importance of instance types for classification , rather than their removability . as far as these estimates of exceptionality
are viable , our results suggest that exceptions should be kept in memory and not be thrown away .      *
the tested criteria can be employed as instance weights as in each @xcite and pebls @xcite , rather than as criteria for instance removal .
instance weighting may add relevant information to similarity matching , and may improve ib1-ig s performance rather than just preserving it .
* the tested implementation of ib1-ig performs a @xmath0-nn search through instance space with @xmath2 .
when @xmath3 , ib1-ig s performance may change , as well as the effect of applying the instance - removal techniques tested here . removing instances from memory may have a less drastic effect when more instance types at more distances are allowed to match a new instance .
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memory - based learning , keeping full memory of learning material , appears a viable approach to learning nlp tasks , and is often superior in generalisation accuracy to eager learning approaches that abstract from learning material . 
 here we investigate three _ partial _ memory - based learning approaches which remove from memory specific task instance types estimated to be exceptional . 
 the three approaches each implement one heuristic function for estimating exceptionality of instance types : ( i ) typicality , ( ii ) class prediction strength , and ( iii ) friendly - neighbourhood size . 
 experiments are performed with the memory - based learning algorithm ib1-ig trained on english word pronunciation . 
 we find that removing instance types with low prediction strength ( ii ) is the only tested method which does not seriously harm generalisation accuracy . 
 we conclude that keeping full memory of types rather than tokens , and excluding minority ambiguities appear to be the only performance - preserving optimisations of memory - based learning .