with reference to the figures , a detailed description of the preferred embodiment is described . with reference to fig1 a , the method according to the present invention of assigning features to nodes of a classification tree is described . the algorithm reads the nodes of an infinite binary tree in the following order : first the root , then the left son , the right son , the left son of the left son , the right son of the left son , etc . a selected characteristic , termed a cost function , having a value c for a feature used in splitting a node is defined by the following equation ( 1 ) based on the minimum description length for the feature at the node : the feature assignment algorithm is initialized at a root node . the steps are outlined below as illustrated in fig1 a . 11 1 . read next feature not already assigned to nodes along the path from the current node to the root , and read its value for all elements in training sample ( block 101 ). 2 . determine whether there are additional features to be tested ( block 102 ). if one exist , then go to the next node in tree and to block 101 ( block 103 ). 3 . let nl and nr denote the number of elements in the training sample whose feature value is 0 ( for left ) and 1 ( for right ), respectively . compute cl and cr from formula ( 1 ) with n and no replaced by nl and nlo , respectively , and nr and nro , respectively , and take the sum of cl + cr ( block 104 ). 5 . if yes , assign to node the current feature ( block 106 ). return to block 101 ( block 107 ). the pruning algorithm for nodes of the tree to which a feature has been assigned is outlined below with reference to fig1 b . 3 . if no , make the node a leaf ( prune ). go to block 205 ( block 203 ). 5 . pick the next node . go to block 201 ( block 205 ). alternatively , the cost function given by equation ( 1 ) may be replaced by the cost function given by the following equation ( 2 ) based on the predictive minimum description length for the node : where ! is a symbol commonly used to designate a factorial operation . because the cost c given by equations ( 1 ) and ( 2 ) is much greater than the the cost of describing features assigned to the nodes , the feature cost may be ignored in these approaches . there is a natural cost function , c ( x t + 1 , x t + 1 ), called the risk , which is defined to be 0 , if the classification is correct ; i . e . if x t + 1 = x t + 1 , and 1 , otherwise . hence , for the entire training sample the risk with some decision tree is the number of misclassifications that have been made . technically , the risk function can still be represented as a predictive code length ( predictive minimum description length ) for a special distribution or as true prediction error , so that the first feature assignment algorithm described above applies with the modification that the cost function c no longer can be calculated from a formula but it has to be evaluated recursively at each node for each examined element in the training sample that reaches that node . ( actually , this is the way even the second of the above given cost functions ( equation ( 2 )), involving the factorials , is preferrably evaluated .) however , the numerical value of the risk is generally speaking a lot smaller than the other cost functions . this involves a complication in the pruning algorithm , because it is no longer justified to ignore the cost of encoding the features themselves that were assigned to the nodes , as was done with the previous cost functions . as a result , we shall describe a new algorithm that does the job of both of the growing and pruning algorithms . but first , we must describe the classification rule x t + 1 ( x t ), where x t = x 1 , . . . , x t denotes the sequence of the class tags of the past elements . we take it as the majority rule : x t + 1 ( x t )= 0 if the number of 0 tags in the past sequence , n 0 ( x t ), satisfies the following condition : either n 0 ( x t )& gt ; t / 2 or in case n 0 ( x t )= t / 2 if n 0 ( x t - 1 )& gt ; t / 2 . otherwise , x t + 1 ( x t )= 1 . at each node we describe the recursive computation of a cost as a function of that portion x of the training sample that has so far arrived at this node . let the feature f split x into the substring w ( f ), whose feature value is &# 34 ; white &# 34 ;, say , and the remaining substring b ( f ), whose feature value under f is &# 34 ; black &# 34 ;. let z denote the next sample arriving at the node . it feature value f ( z ) is either white or black depending on f . in the former case we may call the substring w ( f ) its &# 34 ; past &# 34 ; relative to f , and in the latter case b ( f ) is its past . the past of z is important , because we can use the errors that would have been made to predict the past as an indication of how well we can predict z itself . in fact , let c ( w ( f )) and c ( b ( f )), respectively , denote the risk , determined by the above described majority prediction classification rule , when the past of z is predicted , and let f * be the feature which minimizes this risk . we now define c *( xz )= c *( x ), if the prediction of the class of z is correct when the prediction is done based upon its past , determined by the winning feature f *. otherwise , c *( xz )= c *( x )+ 1 . the feature assigned to the node in question is the last winning feature f * when the entire training sample has been processed . the pruning is done by the rule : prune the tree at the first node along each path from the root where c *( x )& gt ; c ( x ). here , x refers to that portion of the training sample that reaches this node . both risks are defined above . in particular , c ( x ) at the root node is the risk resulting when the training sample is predicted without using any features . at the other nodes it is the risk resulting from prediction when the best feature f * is applied to its father node . in this section we present experimental results obtained by applying the classification scheme based on equation ( 1 ) described above to an automatic character recognition problem . we want to distinguish between two different fonts of the character g . the training - set consists of 40 scanned black - and - white images of each font , each image consisting of 1536 picture - cells ( pels ). fig2 shows a superposition of the 20 images of each font . the integers indicate the relative frequency that a given pel is black in the forty training samples of that font . asterisks show positions which are black in all of the samples , blanks correspond to all white . zeros stand for relative frequencies in the range of 0 to 0 . 1 . the set of features for the classification task was defined as the binary valued content of the individual pels . the application of the splitting and pruning criteria , given in equation ( 1 ), resulted in the tree structured classifier shown in fig3 . the number below the interior nodes designated by circles indicate when pel is the assigned feature to be tested at the node . the number below the terminal nodes designated by squares indicate the class - tag assigned to the leaf . the numbers inside the nodes indicate the number of training samples that reached the node from each of the two classes , with the upper value indicating class 1 and the lower value indicating class 2 . using this tree to classify a new batch of 40 characters , 20 of each font , resulted in an error rate of 6 . 25 percent , which is rather low in view of the high variability of the samples . the present invention provides a new way to design tree structured classifiers . unlike the existing techniques , where the node splitting and pruning rules are derived from a more or less ad hoc basis , the rules according to the preferred embodiment of the present invention result from the application of the intuitively appealing minimum description length mdl principle . according to this , the classification tree is regarded as a model , where the number of parameters grows with the number of nodes . when such a model is used to &# 34 ; explain &# 34 ; the regular features in the training set , formalized by means of the coding of the data , the complexity of the model must be included . this , then , leads to the simple nodewise criterion : compare the total code length needed to describe the class - tags at a node with the shortest combined code length of the son - nodes . if it is smaller , prune the tree at the node ; otherwise , split it . the new pruning scheme is computationally simpler than the cross - validatory pruning methods of mabbet et al ( supra 1980 ) and brieman et al ( supra 1984 ). even if one wishes to perform the pruning only after the tree has been grown to its maximum size , so as to have some look - ahead capabilities , the procedure is still simpler both conceptually and computationally . one has only to compare the code length of any internal node to the sum of the code lengths of its leaves ; if it is smaller the tree is pruned at that node . this comparison can be done efficiently using the postorder data structure , wherein a node &# 39 ; s sub - trees are inspected before the node itself is . the extension of the splitting and pruning criteria to the case of multiple classes is straightforward . nevertheless , when the number of classes is large , the &# 34 ; twoing &# 34 ; method described in brieman et al ( supra 1984 ), at least for the top part of the tree , is preferred because of its efficient implementation . at each node , this scheme finds the two superclasses which in some sense are not dissimilar as well as the best split according to these two superclasses . in fact , the conglomeration of classes into superclasses is very well justified from the mdl principle point of view . notice that the code length of the parameters grows linearly with the number of classes while the code length of the data does not necessarily decrease in such a rate . thus , in general , it seems that the total code length is minimized if classes are grouped to superclasses . indeed , one could even use the mdl principle to find the best superclasses . a preferred implementation of the present invention has been described for the purposes of illustration . those skilled in the art will recognize that modifications and extensions of the methods described herein can be made without departing from the spirit and scope of the invention . it is intended that the invention be defined by the following claims .