This paper introduces a machinery called attributed top-down parsing automaton which performs top{down parsing of strings and, simultaneously, the evaluation of arbitrary noncircular attribute grammars. The strategy of the machinery is based on a single depth{ rst left{to{right traversal over the syntax tree. There is no need to traverse parts of the syntax tree more than once, and hence, the s...

In this paper, we explain the detail analysis procedure of submission 1(Previous predicted results submission) of Task A1. We are trying to induce decision tree models to predict pc:numberOfTenders. Since the type of target attribute is non-negative integer value, we use the variance reduction as the attribute selection criteria. Input attributes are defined based on structure information of Pu...

The majority of existing algorithms for learning decision trees are greedy—a tree is induced topdown, making locally optimal decisions at each node. In most cases, however, the constructed tree is not globally optimal. Even the few non-greedy learners cannot learn good trees when the concept is difficult. Furthermore, they require a fixed amount of time and are not able to generate a better tre...

In this paper we present a new algorithm for filtering a grey-level image using as attribute the number of holes of its connected components. Our approach is based on the max-tree data structure, that makes it possible to implement an attribute filtering of the image with linear computational cost. To determine the number of holes, we present a set of diverse pixel patterns. These patterns are ...

This paper studies the eeects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, M-of-N, and X-of-N representations). To reduce eeects of other factors such as tree learning methods, new attribute search strategies, search starting points, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With diierent option se...

This paper presents a composition method for stack-attributed tree transducers. Stackattributed tree transducers extend attributed tree transducers with a pushdown stack device for attribute values. Stack-attributed tree transducers are more powerful than attributed tree transducers due to the stack mechanism. We extend the existing composition method for attributed tree transducers to the comp...

In the era of huge datasets, the topsearch becomes an effective way to decrease the search time of topobjects. Since we suppose locally accessible data only, the multidimensional indexes containing all attributes together seem to be more effective than a distribution of each attribute to a separate index. Therefore we introduce the topsearch algorithm over grid file – the multidimensional index...

The natural representation of XML data is to use the underlying tree structure of the data. When analyzing these trees we are ensured that no structural information is lost. These tree structures can be efficiently analyzed due to the existence of frequent pattern mining algorithms that works directly on tree structured data. In this work we describe a classification method for XML data based o...