... this problem by
only computing a small set of the most prob-
able parses. The remainder of the sum is es-
timated using a combination of the probabili-
ties from the best parses and the probabilities
2
Cross-entropy ... with our first probability
model.
The first probability model is generative, be-
cause it specifies the joint probability of the in-
put sentence and the output tree. This joint
probability is simply ... pattern where
discriminative probability models are inferior
to generative probability models, but that im-
provements can be achieved by keeping a gener-
ative probability model and training according
to...
... Expression
Classification Using Artificial < /b> Neural < /b> Network < /b> [10], Facial
Expression Classification Using Multi Artificial < /b> Neural < /b>
Network < /b> [11] in the same JAFFE database.
TABLE IV. COMPARATION CLASSIFICATION ...
Classification Using Artificial < /b> Neural < /b> Networks [10] and
Facial Expression Classification Using Multi Artificial < /b>
Neural < /b> Network < /b> [11] (only used ANN).
Beside, this method does not need face boundary detection ...
The MLP uses the algorithm of Gradient Back-Propagation
for training to update W.
B. Structure of MLP Neural < /b> Network < /b>
MLP Neural < /b> Network < /b> applies for seven basic facial
expression analysis signed...
... SNN(s) like the reliability coefficients. Our model links many Neural < /b>
Networks together, so we call it Multi Artificial < /b> Neural < /b> Network < /b> (MANN).
3 Multi Artificial < /b> Neural < /b> Network < /b> apply for image ... Artificial < /b> Neural < /b> Network < /b> (MANN), applying for pattern or image
classification with parameters (m, L), has m Sub -Neural < /b> Network < /b> (SNN) and a global
frame (GF) consisting L Component Neural < /b> Network < /b> ... into
responsive class using a Neural < /b> Network < /b> called Sub Neural < /b> Network < /b> (SNN) of
MANN. Lastly, we use MANN’s global frame (GF) consisting some Component
Neural < /b> Network < /b> (CNN) to compose the classified...
... the neural < /b> network < /b> from the file, or arrange its structure specifying the number of layers and neurons
per layer:
ANNetwork::ANNetwork(const wchar_t *fname);
ANNetwork::ANNetwork(int layers_number, ... plr->get_neurons_number(); n++) {
pnrn = plr->neurons[n];
Articles » General Programming » Algorithms & Recipes » Neural < /b> Networks
Backpropagation Artificial < /b> Neural < /b> Network < /b> in C++
By Chesnokov ... is 0.5) to
obtain the ROC curve on your test set, for example, varying it between 0.0 and 1.0.
Neural < /b> Network < /b> Classes
The neural < /b> network < /b> is composed from the following classes:
ANNetwork
ANNLayer
ANeuron
ANLink
The...
... process which is controlled by the MGA
(Fig. 2). The parameters selected for tuning were the
ARTICLE IN PRESS
b
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b
1
b
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(1-2)
hid.layers
hid.neurons ... of NN models (Yao, 1999). The trend
has been towards indirect encoding due to its better
scalability for example, but the direct encoding
can be suitable for the precise and fine-tuned search
of ... quality can be considered as a
non-linear regression problem between predictors (such
as meteorological and air quality variables) and
predictand (in this case, hourly concentration). Neural
< /b> networks,...
... artificial < /b> Neural < /b> Network < /b> (MPNN) and the Kohonen neural < /b>
network < /b> (KNN). Both can be replaced by other artificial < /b> neural < /b> networks for the same
purpose, but this does not change the method of using ... reconstruct arbitrary multivariable and highly non-linear functions. Therefore it is a
suitable tool for modelling atmospheric phenomena whose behaviour has not yet been
described by formulas but is ... Conclusion
Two types of artificial < /b> neural < /b> networks were shown to be useful tools for environmental
modelling: the multilayer perceptron neural < /b> network < /b> MPNN and the Kohonen neural < /b>
network < /b> KNN. MPNN...
... 2-8 percent by weight in the leaves which
becomes the primary psychoactive ingredient in tobacco
smoke. It is very soluble in water and nonpolar solvents.
It is absorbed rapidly by the body, either ... outputs of a biological
systems can be rather complex and could be either deter-
ministic or stochastic or both, thus it is crucial that this
complexity measure be able to handle both signal types.
The ...
receptor abnormalities in alzheimer's disease. Biol Psychiatry 2001,
49:175-184.
21. Ono K, Hasegawa K, Yamada M, Naiki H: Nicotine breaks down
preformed alzheimer's beta-amyloid fibrils...
... predictability
of the warp
breakage rate
from a sizing yarn
quality index
using a feed-
forward back-
propagation
network < /b> in an
artificial < /b> neural < /b>
network < /b> system
/
14 Artificial < /b> Neural < /b>
Network-< /b> embedded ... data
resulted in better the mapping of the network,< /b> and better predictions. Therefore, airbag
fabrics could be successfully engineered using artificial < /b> neural < /b> network.< /b>
3.2 Fabric-property prediction ...
establish
translation
equations for total
hand evaluations
of fabrics by
employing
a stepwise
re
g
ression method
and an artificial < /b>
neural < /b> network < /b>
/
19 Artificial < /b> neural < /b>
network-< /b> based...
... this section, the standard BP neural < /b> network < /b> and the
improved BP neural < /b> network < /b> GA-BP II and GA-BP III are used to optimize the hot pressing
parameters of ZrO
2
/TiB
2
/Al
2
O
3
namo-micro-composite ... GA-BP II algorithm and 34% and 24% of that of standard BP algorithm.
The least relative error of hardness is 0.9% obtained by GA-BP III algorithm which is the same
as that obtained by GA-BP ... the structure
of the BP neural < /b> network < /b> which includes the number of neurons in input layer, hidden layer
and output layer, and the number of the hidden layer, the BP neural < /b> network < /b> model of the...
... the neural < /b> network < /b>
2.2 Neural < /b> Network < /b>
In this subsection, text extraction from static image
or video sequences is accomplished using the
back-propagation (BP) algorithm on a neural < /b> network.< /b> ... network.< /b>
The training of the neural < /b> network < /b> is based on the
features we obtain from the DWT detail component
sub-bands. As shown in Figure 6, the proposed neural < /b>
network < /b> architecture is simpler ... a
neural < /b> network < /b> for training based on the
back-propagation algorithm for neural < /b> networks.
After the neural < /b> network < /b> is well trained, new input
data will produce an output value between zero...
... magnitude image obtained from the original
image is divided into a grid of blocks. The blocks are
classified as text block or non-text block based on the total
number of edges in the block. The ... For each principal color, a binary image is
created and an RLSA is used to form object blocks which
are then classified as text blocks or non-text block based on
the block characteristics. All ... CONCLUSION AND FUTURE
WORKS
A neural < /b> network < /b> based method is discussed in this
paper. The features used for the neural < /b> network < /b> are not
only the spatial characteristics but also the relative
alignment...
... that set by ASTM D4186.
DESIGN ARTIFICIAL < /b> NEURAL < /b> NETWORK < /b> MODEL
Neural < /b> networks are computer models that mimic the knowledge
acquisition and organization skills of the human brain. Since, ...
complex problems in many fields. In this study, a back-propagation
neural < /b> network < /b> model for estimating of proper strain rate form soil
parameter is proposed. The back-propagation neural < /b> network < /b> ... consolidation will be pronounced. On
the other hand, if pore pressures become excessive, assumptions made
in deriving the theory will again be rendered invalid because the pore
pressure distribution will...