Search results for "Mach"

Wear of Ceramic Tools When Working Nickel Based Alloys

1996

In order to improve the toughness of alumina materials, various trials have recently been made. These include toughening by the addition of zirconia and of significant amounts of titanium carbide to ceramic oxide Al2O3 and the more recent use of nitride based ceramics, which have resulted in an increase of fracture toughness and in a significant improvement of ceramic tool performance. Another very recent way of improving ceramic materials consists in adding SiC whiskers to Al2O3 matrix. This composite material is also suitable for machining nickel based alloys. In order to evaluate and to qualify these materials some test cycles have been carried out in continuous cutting conditions, emplo…

Computer-Aided Diagnosis System with Backpropagation Artificial Neural Network—Improving Human Readers Performance

2016

This article presents the results of a study into possibility of artificial neural networks (ANNs) to classify cancer changes in mammographic images. Today’s Computer-Aided Detection (CAD) systems cannot detect 100 % of pathological changes. One of the properties of an ANN is generalized information —it can identify not only learned data but also data that is similar to training set. The combination of CAD and ANN could give better result and help radiologists to take the right decision.

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…

Ensemble Feature Selection Based on the Contextual Merit

2001

Recent research has proved the benefits of using ensembles of classifiers for classification problems. Ensembles constructed by machine learning methods manipulating the training set are used to create diverse sets of accurate classifiers. Different feature selection techniques based on applying different heuristics for generating base classifiers can be adjusted to specific domain characteristics. In this paper we consider and experiment with the contextual feature merit measure as a feature selection heuristic. We use the diversity of an ensemble as evaluation function in our new algorithm with a refinement cycle. We have evaluated our algorithm on seven data sets from UCI. The experiment…

Putting the user into the active learning loop : Towards realistic but efficient photointerpretation

2012

In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according to their uncertainty, and the confidence of the user in labeling, which is related both to the homogeneity of the pixel context and to the knowledge of the user of the scene. In this paper, we propose a two-steps procedure based on a filtering scheme to learn the confidence of the user in labeling. This way, candidate training pixels are ranked according both to their uncertainty and to the chances of being labeled correctly by the user. In…

2004

This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to …

Multilayer neural networks: an experimental evaluation of on-line training methods

2004

Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…

Intelligent Sampling for Vegetation Nitrogen Mapping Based on Hybrid Machine Learning Algorithms

2021

Upcoming satellite imaging spectroscopy missions will deliver spatiotemporal explicit data streams to be exploited for mapping vegetation properties, such as nitrogen (N) content. Within retrieval workflows for real-time mapping over agricultural regions, such crop-specific information products need to be derived precisely and rapidly. To allow fast processing, intelligent sampling schemes for training databases should be incorporated to establish efficient machine learning (ML) models. In this study, we implemented active learning (AL) heuristics using kernel ridge regression (KRR) to minimize and optimize a training database for variational heteroscedastic Gaussian processes regression (V…

Learning the structure of HMM's through grammatical inference techniques

2002

A technique is described in which all the components of a hidden Markov model are learnt from training speech data. The structure or topology of the model (i.e. the number of states and the actual transitions) is obtained by means of an error-correcting grammatical inference algorithm (ECGI). This structure is then reduced by using an appropriate state pruning criterion. The statistical parameters that are associated with the obtained topology are estimated from the same training data by means of the standard Baum-Welch algorithm. Experimental results showing the applicability of this technique to speech recognition are presented. >