C4I Interfaces to Simulations are limited in functionality. One of the principle factors causing this is a lack of interface standards. While standards are a necessary condition, they are not sufficient. In order to have a complete interface, the data to be exchanged must be represented in both systems (C4I and Simulation). However, the current status is that C4I systems and simulations 1) do n...

This paper provides a systematic study of incremental learning from noise-free and from noisy data. As usual, we distinguish between learning from positive data and learning from positive and negative data, synonymously called learning from text and learning from informant. Our study relies on the notion of noisy data introduced by Stephan. The basic scenario, named iterative learning, is as fo...

This paper provides a systematic study of incremental learning from noise-free and from noisy data, thereby distinguishing between learning from only positive data and from both positive and negative data. Our study relies on the notion of noisy data introduced in 22]. The basic scenario, named iterative learning, is as follows. In every learning stage, an algorithmic learner takes as input one...

Data parallelism is currently one of the most successful models for programming massively parallel computers. The central idea is to evaluate a uniform collection of data in parallel by simultaneously manipulating each data element in the collection. Despite many of its promising features, the current approach su ers from two problems. First, the main parallel data structures that most data par...

Clustering is a task of assigning a set of objects into groups called clusters. In general the clustering algorithms can be classified into two categories. One is hard clustering; another one is soft (fuzzy) clustering. Hard clustering, the data’s are divided into distinct clusters, where each data element belongs to exactly one cluster. In soft clustering, data elements belong to more than one...

The observation that a significant class of data processing and analysis applications can be expressed in terms of a small set of primitives that are easy to parallelize has resulted in increasing popularity of batch-oriented, highly-parallelizable cluster frameworks. These frameworks, however, are known to have shortcomings for certain application domains. For example, in many data analysis ap...

Competitive learning is well-known method to process data. Various goals may be achieved using competitive learning such as classification or vector quantization. In this paper, we present a different insight into the principle of supervised competitive learning. An innovative approach to the supervised self-organization is suggested. The method is based on different handling of input data labe...