We classify descent algorithms for nonlinear programs and variational inequalities, based on their characterizations as cost approximation algorithms; an eight-tuple of parameters describes the problem and the choices of cost approximating mappings and merit functions. The taxonomy is illustrated on classical algorithms and is utilized to interrelate known algorithm frameworks.

Planning problems are naturally knowledge rich. Yet, most planning research to date has focused on languages and algorithms to represent and search through the decision space in order to generate a feasible solution. This paper surveys previous work on extending planning algorithms with expressive representations of knowledge, in particular description logics, to reasoning about action, plan, a...

Recently, the information community has seen the emergence of user-centric media applications, which are characterized by the central position given to the user. To fulfill the user-centric promise, it is necessary to understand and model the actions of the users of the system. This position paper presents a methodology for modeling the behavior of multimedia database users. To this end, we pro...

We describe eecient algorithmic proposals to approach three fundamental problems in data mining: association rules, episodes in sequences, and generalized association rules over hierarchical taxonomies. The association rule discovery problem aims at identifying frequent itemsets in a database and then forming conditional implication rules among them. For this association task, we will introduce...

Text categorization is the classification to assign a text document to an appropriate category in a predefined set of categories. We present an approach on hierarchical text categorization that is a recently emerged subfield of the main topic. Here, documents are assigned to leaf-level categories of a category tree (called taxonomy). The algorithm applies an iterative learning module that allow...

Taxonomies, such as Library of Congress Subject Headings and Open Directory Project, are widely used to support browsing-style information access in document collections. We call them browsing taxonomies. Most existing browsing taxonomies are manually constructed thus they could not easily adapt to arbitrary document collections. In this paper, we investigate both automatic and interactive tech...

With the increased use of ontologies in semantically-enabled applications, the issue of debugging defects in ontologies has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Debugging consists of the phases of detection and repairing. In this paper we focus on the repairing phase of a particular kind of defects, i.e. the missing relations...

Clustering is one of the most important data mining techniques that partitions data according to some similarity criterion. The problems of clustering categorical data have attracted much attention from the data mining research community recently. As the extension of the k-Means algorithm, the k-Modes algorithm has been widely applied to categorical data clustering by replacing means with modes...

Contextual information plays a key role in the automatic interpretation of text. This paper is concerned with the identification of textual contexts. A context taxonomy is introduced first, followed by an algorithm for detecting context boundaries. Experiments on the detection of subjective contexts using a machine learning model were performed using a set of syntactic features.

Precise recommendation of followers helps in improving the user experience and maintaining the prosperity of twitter and microblog platforms. In this paper, we design a hybrid recommender system of microblog as a solution of KDD Cup 2012, track 1 task, which requires predicting users a user might follow in Tencent Microblog. We describe the background of the problem and present the algorithm co...