... particular class of
models, activity -based models, which view a project as a
process, decomposed into a networkof activities.
Thus, we do not include in our survey most systems
dynamics models (e.g., ... the variety of models proposed in the literature, a survey of the PD
process modeling literature is timely and valuable. In this work, we focus on the activity network- based
process models that ... ge-
neric activity network. We could not find an activity
network- basedmodel that provides an estimate of the
“flexibility index” for PD processes, even though it
would seem that a modelof what work...
... generated by simulation of a conventional
SVM algorithm, and then a backpropagation technique in
the MATLAB -based NeuralNetwork Toolbox [8] was used
for offline training. The network was simulated ... Chairman of the IEEE Industry
Applications Society (IAS) Industrial Power Converter Committee, and IAS
member of the NeuralNetwork Council. He has been a Member of the Editorial
Board of the P
ROCEEDINGS ... VOL. 38, NO. 3, MAY/JUNE 2002
Fig. 7. Feedforward neural- network (1–24–12) -based space-vector PWM controller.
Fig. 8. Segmentation ofneuralnetwork output for
U
-phase
P
states.
and signals...
... Estimating Probabilities of English
Bigrams. Computer Speech & Language, 5(1):19–54.
Joshua Goodman. 2001. A Bit of Progress in Language
Modeling. Computer Speech & Language, 15(4):403–
434.
Bo-June ... Improved
Backing-off for M-Gram Language Modeling. In Pro-
ceedings of International Conference on Acoustics,
Speech, and Signal Processing.
Robert C. Moore and William Lewis. 2010. Intelligent
selection oflanguage ... k
train
(w) denote the number of
occurrences of w in the training corpus, and k
test
(w)
denote the number of occurrences of w in the test
corpus. We define the empirical discount of w to be
d(w) = k
train
(w)...
... treebank which con-
sists of about 50000 sentences of newspaper text.
2.3 Robustness Issues
A major problem of grammar -based approaches
to language modeling is how to deal with out -of-
grammar utterances. ... reduced
model is weakly significant on a level of 2.6% for
the MAPSSWE test.
For both models, the optimal value of q was 0.001
for almost all training runs. The language model
weight µ of the reduced ... unpacked. For 24 of the 447
lattices, some of the N best hypotheses contained
phrases with more than 1000 readings. For these lat-
tices the grammar -based languagemodel was sim-
ply switched off in the...
... third neuralnetwork combines the ad-
vantages of the generative probability model
with the advantages of the discriminative opti-
mization criteria. The structure of the network
and the set of ... use a history -based modelof parsing. De-
signing a history -based modelof parsing in-
volves two steps, first choosing a mapping from
the set of phrase structure trees to the set of
parses, and ... several networks for each of the
GSSN models and chose the best ones based on
their validation performance. We then trained
one network for each of the DGSSN models
and for the DSSN model. The...
... 6: Comparison of word umgram, bigram
and MI-Trigger model
In order to evaluate the efficiency of MI-
Trigger -based language modeling, we compare it
with word unigram and bigram models. Both ... and bigram models. The conditional
perplexity of the DD-6-MI-Trigger model is less
than that of word bigram model and much less
than the word unigram model.
• The parameter number of the MI-Trigger ... frequency of word
pairs as a function of distance
To compare the effects of the above two
factors, 20 MI-trigger models(in which DI and
DD MI-Trigger models with a window size of 1
are same)...
... billions of tokens of training data.
We then show that using partially class -based lan-
guage models trained using the resulting classifica-
tions together with word -based language models in
a state -of- the-art ... built
just like word -based n-gram models using existing
infrastructure. In addition, the size of the model is
usually greatly reduced.
2.1 One-Sided Class -Based Models
Two-sided class -based models received ... num-
ber of parameters of the model (Brown et al., 1990).
They have often been shown to improve the per-
formance of speech recognition systems when com-
bined with word -based language models (Martin...
... and tested the neural network.
The resulting sum of squared errors made by the
network is an indication of how important that por-
tion of the image is for the detection task. Plots of
the error ... for four networks
working alone, the effect of overlap eliminationand
collapsing multiple detections, and the results of us-
ing ANDing, ORing, voting, and neural network
arbitration. Networks ... face.
Examplesof outputfroma single network are shown
in Figure 2. In the figure, each box represents the
position and size of a window to which the neural
network gave a positive response. The network...
... artificial neural
network group -based adaptive tolerance (GAT) trees. IEEE Transactions on Neural Networks,
7(3):555–567, 1996.
13
Figure 4: Left: Average ofuprightface examples. Right: Positionsof average ... recognition/detection by probabilis-
tic decision -based neural network. IEEE Transactions on Neural Networks, Special Issue on
Artificial Neural Networks and Pattern Recognition, 8(1), January 1997.
[
Moghaddam ... 8: Detection of faces rotated out -of- plane.
11
Rotation Invariant Neural Network- Based
Face Detection
Henry A. Rowley Shumeet Baluja Takeo Kanade
December 1997
CMU-CS-97-201
School of Computer...
... outputs. The class of multi-layer networks as a
whole can represent any desired function of a set
of attributes, and signatures can be readily modeled
as a function of a set of attributes.
2) ... with Hidden Markov Models. Proceedings
of the 14th International Conference on Pattern Recognition,
Brisbane, Australia, pp 1309–1312, 1998.
[5] L. Fausett. Fundamentals ofNeural Networks. Prentice ... experimentation,
the above stopping condition caused the training of the
network to cease in a reasonable amount of time (a
maximum of a number of minutes) in every instance. In
practice it may be necessary...
... Neural Networks (NN), offline, Signature
Recognition, etc.
I. INTRODUCTION :
The aim of off-line signature verification is to decide,
whether a signature originates from a given signer based ... However, in HSV most of
the subtle nuances of the writing such as size and slant are
indicative of the signer’s natural style, removal of which
would deny the HSV system of useful information. ... to the NN.
International Journal of Engineering and Advanced Technology (IJEAT)
ISSN: 2249 – 8958, Volume-1, Issue-1, October 2011
73
Neural Network- based Offline Handwritten
Signature Verification...