When building classiication models, it is common practice to prune them to counter spurious eeects of the training data: this often improves performance and reduces model size. \Reduced-error pruning" is a fast pruning procedure for decision trees that is known to produce small and accurate trees. Apart from the data from which the tree is grown, it uses an independent \pruning" set, and prunin...

This paper presents a new algorithm, \Pressure Search," for growing min-max game trees. The algorithm is based on the idea of best-rst search. The goal of the search is to nd a strategy which will change the estimated value of the current position. The amount of pressure, deened as inversely proportional to the number of options available to the opponent, is used as a heuristic measure of the r...

The paper concerns the problem of Boolean satisfiability checking, which is recognized as one of the most important issues in the field of modern digital electronic system verification and design. The paper analyzes different strategies and scenarios of the proving process, and presents a modified and extended version of the author’s FUDASAT algorithm. The original FUDASAT methodology is an int...

Before symbolic rules are extracted from a trained neural network, the network is usually pruned so as to obtain more concise rules. Typical pruning algorithms require retraining the network which incurs additional cost. This paper presents FERNN, a fast method for extracting rules from trained neural networks without network retraining. Given a fully connected trained feedforward network, FERN...

Considerable efforts have been spent in studying subgraph problem. Traditional subgraph containment query is to retrieve all database graphs which contain the query graph g. A variation to that is to find all occurrences of a particular pattern(the query) in a large database graph. We call it subgraph matching problem. The state of art solution to this problem is GADDI. In this paper, we will p...

ÐMining association rules on large data sets has received considerable attention in recent years. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. ...

Several techniques are known for reducing the size of language models, including count cutoffs [1], Weighted Difference pruning [2], Stolcke pruning [3], and clustering [4]. We compare all of these techniques and show some surprising results. For instance, at low pruning thresholds, Weighted Difference and Stolcke pruning underperform count cutoffs. We then show novel clustering techniques that...

Pruning is an eeective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the eld of Inductive Logic Programming. However, it has been shown that these methods can be very ineecient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these...

the application of organic sorbents for removing heavy metal from water, replacing expensive sorbents, is particularly appropriate for developing countries. to investigate the efficiency of some organic sorbents in removing of cd2+ from aqueous solution, an experiment was conducted with three organic sorbents (sunflower stalks, apple and grapevine pruning residues) and 11 initial concentration ...

Nowadays, web archives preserve the history of large portions of the web. As medias are shifting from printed to digital editions, accessing these huge information sources is drawing increasingly more attention from national and international institutions, as well as from the research community. These collections are intrinsically big, leading to index files that do not fit into the memory and ...