Search results for "UML"

Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification

2012

Aspects Concerning SVM Method’s Scalability

2008

In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining a good classifier. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that increases exponentially are occurring. In this paper we are presenting a method that allows working with huge data sets into the training step without increasing exponentially the training time and without significantly decreasing the classification accuracy.

k-Truss Decomposition for Modular Centrality

2018

There is currently much interest in identifying influential spreaders in complex networks due to many applications concerned, such as controlling the outbreak of epidemics and conducting advertisements for commercial products, and so on. A plethora of centrality measures have been proposed over the years based on the topological properties of networks. However, most of these classical centrality measures fail to select the most influential nodes in networks with a modular structure despite that it is an omnipresent property in real-world networks. Few authors have introduced centrality measures tailored to networks with community structure. In a recent work, we have shown that, in this case…

ENFORCEMENT OF INTER-TASK DEPENDENCIES IN WORKFLOWS, CHARACTERIZATION AND PARADIGM

1998

Workflow techniques have gained a lot of attention as a means to support advanced business applications such as cooperative information systems and process re-engineering but also as a means to integrate legacy systems. Inter-task dependencies, described separately from the other parts of the workflow, have been recognized as a valuable method in describing certain restrictions on the executions of workflows. In this paper, we study the issue of pre-analysing and enforcing inter-task dependencies. The protocol and the theory behind it are presented, along with examples and discussions on ways to improve the performance. The idea is to present the meaning of a dependency through an automato…

nalysis, Modeling and Simulation of Mechatronic Systems using the Bond Graph Method

2011

The Bond Graph is the proper choice of physical system used for: (i) Modeling which can be applied to systems combining multidisciplinary energy domains, (ii) Analysis to provide a great value proposition for finding the algebraic loops within the system enabling the process of troubleshooting and eliminating the defects by using the proper component(s) to fix the causality conflict even without being acquainted in the proper system, and (iii) Simulation facilitated through derived state space equations from the Bond Graph model is solved using industrial simulation software, such as 20-Sim. The Bond Graph technique is a graphical language of modeling, in which component energy ports are co…

A Mechanistic Reading of Quantum Laser Theory

2015

Quantum laser theory proceeds in a way that seems at variance with the mechanistic model of explanation. First, as is typical for a complex systems theory, the detailed behavior of the component parts plays a surprisingly subordinate role. In particular, the so-called “enslaving principle” seems to defy a mechanistic reading. Moreover, being quantum objects, the “parts” of a laser are neither located in space nor describable as separate entities. I want to show that, despite these apparent obstacles, quantum laser theory constitutes a good example of a mechanistic explanation in a quantum physical setting, provided that one broadens the notion of mechanism. One may feel that such adjustment…

Researches on Grain Harvesting Machines

1970

The purpose of the paper is to present some researches made on the mechanisms of the combine for cutting, feeding and threshing. There are presented the floating cutter bar, the combine with a spike-tooth cylinder and rotary separation, the combine with dual threshing and separating rotors.
 Some of the adjustments are made on the finger sensors for cutter bar. One of the most important adjustment is that made in order to reduce harvest loss. Forward speed is one of the main factor in optimizing the performance of a combine harvester. The method used in the paper is that of direct observing and measuring made on different component parts of combines.

Ultimate Order Statistics-Based Prototype Reduction Schemes

2013

Published version of a chapter in the book: AI 2013: Advances in Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-319-03680-9_42 The objective of Prototype Reduction Schemes (PRSs) and Border Identification (BI) algorithms is to reduce the number of training vectors, while simultaneously attempting to guarantee that the classifier built on the reduced design set performs as well, or nearly as well, as the classifier built on the original design set. In this paper, we shall push the limit on the field of PRSs to see if we can obtain a classification accuracy comparable to the optimal, by condensing the information in the data set into a single tr…

Feature Selection for Ensembles of Simple Bayesian Classifiers

2002

A popular method for creating an accurate classifier from a set of training data is to train several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. However, the simple Bayesian classifier has much broader applicability than previously thought. Besides its high classification accuracy, it also has advantages in terms of simplicity, learning speed, classification speed, storage space, and incrementality. One way to generate an ensemble of simple Bayesian classifiers is to use different feature subsets as in the random subspace method. In this paper we present a technique for building ensembles o…

Learning Similarity Scores by Using a Family of Distance Functions in Multiple Feature Spaces

2017

There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine lea…