Semantic analysis of multimedia content is an on going research
area that has gained a lot of attention over the last few years.
Additionally, machine learning techniques are widely used for multimedia
analysis with great success. This work presents a combined approach
to semantic adaptation of neural network classifiers in multimedia framework.
It is based on a fuzzy reasoning engine which is able to evaluate
the outputs and the confidence levels of the neural network classifier, using
a knowledge base. Improved image segmentation results are obtained,
which are used for adaptation of the network classifier, further increasing
its ability to provide accurate classification of the specific content.
對(duì)應(yīng)分析correspondence analysis(ANACOR)
[G,F,A]=ANACOR(X),X為原始數(shù)據(jù) p X n維 即有n個(gè)樣本,每個(gè)樣本由p個(gè)變量來(lái)描述。返回F為R型因子分析后的結(jié)果,Q為最后的結(jié)果,A=ZZ 。
BP neural network for time series analysis predicted that by entering the corresponding time-series data to predict the future, suitable for beginners on the BP neural network learning
A stability analysis is presented for staggered schemes for the governing equations of compressible flow. The
method is based on Fourier analysis. The approximate nature of pressure-correction solution methods is taken into
account. 2001 IMACS. Published by Elsevier Science B.V. All rights reserved