We describe computationally efficient meth­ ods for learning mixtures in which each com­ ponent is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple search-and-score algorithms are infeasible for a variety of problems, and in­ troduce a feasible approach in which param­ eter and structure search is interleaved and expected data is treated as real data. Our app...

We present a randomized DNA algorithm for k SAT based on the classical algorithm of Paturi et al For an n variable m clause instance of k SAT m n our algorithm nds a satisfying assignment assuming one exists with probability e in worst case time O k mn and space O k n log This makes it the most space e cient DNA k SAT algo rithm for k and k n log i e the clause size is small compared to the num...

Mining of association rules became one of the strongest elds of data mining This paper presents a classi cation rule learning algo rithm APRIORI C upgrading APRIORI to dealing with classi cation problems decreasing its memory consumption and time complexity fur ther decreasing its time complexity by feature subset selection and im proving the understandability of results by rule post processing...

Inferences in directed acyclic graphs associated with probability intervals and sets of probabil­ ities are NP-hard, even for polytrees. We pro­ pose: I) an improvement on Tessem's AIR algo­ rithm for inferences on polytrees associated with probability intervals; 2) a new algorithm for ap­ proximate inferences based on local search; 3) branch-and-bound algorithms that combine the previous techn...

In the field of Artificial Intelligence, traditional approaches . to choosing moves In games involve the use of the minimax algo­ rithm. However, recent research results indi­ cate that minimaxing may not always be the best approach. In this paper we summarize the results of some measurements on several model games with several different evaluation functions. These measurements, which are prese...

presented here is a generalization of the implicit enumeration algorithm that can be applied when the objec-tive function is being maximized and can be rewritten as the difference of two non-decreasing functions. also developed is a computational algorithm, named linear speedup, to use whatever explicit linear constraints are present to speedup the search for a solution. the method is easy to u...

Object tracking is still a challenging task, especially if it is d o ne in a realistic env iro nm ent. T he o ngo ing increase o f co m pu tatio nal po w er and the efficiency o f the algo rithm s allo w real-tim e estim atio n o f the o bject’s po se in six d egrees o f freed o m . One o f these algo rithm s is the 3 -D hyperplane appro ach, w hich is u sed thro u gho u t this paper, as it has...

T he leading par tit ional clustering technique, K Modes, is one of the most computationally eff icient clustering methods fo r categ orical data. In the t raditional K Modes algo rithm, the simple matching dissim ilarity measure is used to compute the distance betw een two values of the same catego rical at t ributes. T his compares tw o categorical v alues directly and results in either a dif...

The gaining returns in line with risks is always a major concern for market play-ers. This study compared the selection of stock portfolios based on the strategy of buying and retaining winning stocks and the purchase strategy based on the level of investment risks. In this study, the two-step optimization algorithms NSGA-II and SPEA-II were used to optimize the stock portfolios. In order to de...

A new learning algorithm for feedforward networks, learning by choice of intern al represent at ions (C HIR), was recently introduced [1,2]. W hereas many algor it hm s red uce th e learning proce ss to minimizing a cost function over t he weights, our method treats th e internal representations as the funda ment al ent it ies to be determi ned. T he algo rithm applied a sea rch procedure in th...