Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks about this model: (1) the problem of word clustering, it is hard to find a suitable clustering method that has good performance and not large amount of computation. (2) class-based method always loses some prediction ability to adapt the text of different domain. The...

In recent years, the advance of information and communication technologies has allowed the storage and transfer of massive amounts of data. The availability of this overwhelming amount of data stimulates a growing need to develop fast and accurate algorithms to discover useful information hidden in the data. This need is even more acute for unsupervised data, which lacks information about the c...

Feature selection for clustering is a problem rarely addressed in the literature. Although recently there has been some work on the area, there is a lack of extensive empirical evaluation to assess the potential of each method. In this paper, we propose a new implementation of a wrapper and adapt an existing filter method to perform experiments over several data sets and compare both approaches...

In this paper we describe a word clustering method for class-based n-gram model. The measurement for clustering is the entropy on a corpus di erent from the corpus for n-gram model estimation. The search method is based on the greedy algorithm. We applied this method to a Japanese EDR corpus and English Penn Treebank corpus. The perplexities of word-based n-gram model on EDR corpus and Penn Tre...

The paper deals with a distance based multi-relational clustering application in a real data case study. A novel method for a dissimilarity matrix calculation in multirelational settings has been proposed and implemented in R language. The proposed method has been tested by analyzing publications related to data mining subject and indexed in the medical index database MedLine. Clustering based ...

In this work, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-le...

We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...

Non-maximum suppression (NMS) is a key post-processing step in many computer vision applications. In the context of object detection, it is used to transform a smooth response map that triggers many imprecise object window hypotheses in, ideally, a single bounding-box for each detected object. The most common approach for NMS for object detection is a greedy, locally optimal strategy with sever...