Examples of using Pattern classification in English and their translations into Portuguese
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This work presents a methodology directed to pattern classification problems.
This skeletal pattern classification stems from the need to ensure the use of discrete approaches based on the diagnosis, treatment and prognosis of each group.
Is it not possible that what we call artistic training is essentially training for pattern classification?
In this work a new method of data clustering and pattern classification based on principal curves is presented.
We present function models built by support vectors,a class of machine learning methods that can be used to pattern classification or regression.
Numerical results are presented for pattern classification problems using real bases of large and small dimensions.
This work discusses the use of traditional and hybrid optimization techniques in the context mono andmulti-objective for optimization of the problem of pattern classification in ensembles.
Design processes relevant to technologies such as networks and pattern classification and supporting methodologies such as system design and software engineering.
Pattern classification is a supervised learning problem in the field of science known as pattern recognition, through which to discriminate data instances in different classes.
In this work, we intend to characterize the morphology of polymer thin films by techniques of image processing,mainly using computational geometry and pattern classification.
The problem of pattern classification is treated as a problem of optimization looking to find the subset of attributes and classifiers of problem that minimizes the classification error of the ensemble.
To answer the third and last question, the present study used three techniques ANN, GA and SVM,all of which are related to pattern classifications. The ANN technique had the best performance for this case study.
These pattern classification algorithms are able to incorporate previous knowledge in the form of similarity functions, i.e., a kernel, and it has been successful in a wide range of supervised learning problems.
This work proposes an approach that employs image processing techniques and pattern classification for the recognition of digits displayed in the counter of consumption of natural gas meters.
To this end, we simulated annotation errors, which map a temporal sequence of neuronal activations(obtained by microelectrode implants) to the contact intervals of an animal with external stimulus(objects), changing the original an- notation values,with the purpose of evaluating the effect of such errors on the quality of pattern classification, when considering a set of computational techniques.
This dissertation deals with pattern classification problems, which aim to create separating surfaces along the pattern space dividing it in regions according to the pattern classes.
The methodology is based on an analysis of the communication features of the profinet protocol and identifying and pattern classification, which is one of the main applications of the use of artificial neural networks ann.
It was also developed a comparative study of methods of pattern classification for the identification of defects has been developed in these machines, such as naive bayes, k- nearest neighbor, support vector machine(sequential minimal optimization), artificial neural network(multilayer perceptron), repeated incremental pruning to produce error reduction and c4.5 decision tree.
The results of this study showed an association between the score assigned to each individual for esthetic facial profile and facial pattern classification, facial convexity angle and lower face angle Table 3.
For interpretation is presented an intelligent algorithm based on the competitive generalized angular neural network,built for angular pattern classification or data clustering in ndimensional space that have an approximately ellipsoidal envelope, which are the characteristics of clusters in the vsh-l-k plot and make your visual interpretation extremely complex.
This dissertation investigates the use of two different methods- neural network multilayer perceptrons(multi-layered perceptron, mlp) diffuse and random forest(random forest,rf)- the deforestation pattern classification in the brazilian amazon using modis images. in this sense, deforestation maps were generated from various sizes.
One of the problems observed concerns the classification of homogeneous nuclear pattern regarding the nucleolus reactivity and the mixed pattern classification, approaching multiple reactivities in the same group, e.g., a pattern with two or more autoantibodies against nuclear antigens.
The thesis presents two new methodologies for adjust phase for models based on support vectormachines(support vector machines- svm) applied both in pattern classification problems, as in regression problems(support vector regression- svr), and a methodology of obtaining alternative margins existing for svr models.
This work develops new approaches for adaptive fuzzy rule-based system modeling. the approaches comprise methods capable to perform systems identification,control, pattern classification and time-series forecasting in non stationary dynamic environments by updating the model structure an its parameters using data streams.
However, as the evaluation of the ureteric jet is based on the observation of a spectral curve,difficulties in its interpretation may be raised because of individual variations among investigators with regards to patterns classification caused by different understanding of what characterizes the different patterns. .
In particular, the Brazilian group has taken the lead on building a nation-wide consensus of the ANA pattern nomenclature classification.
This work has as purpose to verify the pattern of classification categories associated with meteorological systems which dominate the wind circulation at santos basin in são paulo/brazil.
In classification, a pattern of activity across multiple voxels is used to determine the particular class from which the stimulus was drawn.