selects the mux channel and configures the MAX197 for
second write pulse, written with ACQMOD = 0, termi-
either unipolar or bipolar input range. A write pulse (WR
nates acquisition and starts conversion on WR°Os risin
+ CS) can either start an acquisition interval or initiate a
edge (Figure 6). However, if the second control byte
combined acquisition plus conversion. The sampling
contains ACQMOD = 1, an indefinite acquisition interval
interval occurs at the end of the acquisition interval.
is restarted.
The ACQMOD bit in the input control byte offer
selects the mux channel and configures the MAX197 for
second write pulse, written with ACQMOD = 0, termi-
either unipolar or bipolar input range. A write pulse (WR
nates acquisition and starts conversion on WR°Os risin
is restarted.
The ACQMOD bit in the input control byte offer+ CS) can either start an acquisition interval or initiate a
edge (Figure 6). However, if the second control byte
combined acquisition plus conversion. The sampling
contains ACQMOD = 1, an indefinite acquisition interval
interval occurs at the end of the acquisition interval.
We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.
Batch version of the back-propagation algorithm.
％ Given a set of corresponding input-output pairs and an initial network
％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the
％ network with backpropagation.
％
％ The activation functions must be either linear or tanh. The network
％ architecture is defined by the matrix NetDef consisting of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
％
％ Train a two layer neural network with the Levenberg-Marquardt
％ method.
％
％ If desired, it is possible to use regularization by
％ weight decay. Also pruned (ie. not fully connected) networks can
％ be trained.
％
％ Given a set of corresponding input-output pairs and an initial
％ network,
％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms)
％ trains the network with the Levenberg-Marquardt method.
％
％ The activation functions can be either linear or tanh. The
％ network architecture is defined by the matrix NetDef which
％ has two rows. The first row specifies the hidden layer and the
％ second row specifies the output layer.
Train a two layer neural network with a recursive prediction error
％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
％ connected) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the matrix NetDef , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
The ZZIPlib provides read access on ZIP-archives. The library uses only the patent-free compression-algorithms supported by Zlib. It provides functions that transparently access files being either real files or zipped files, both with the same filepath
This program is free software you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation either version 2 of the License, or
(at your option) any later version.
This program is free software you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation either version 2 of the License, or
(at your option) any later version.
This program is free software you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation either version 2 of the License, or
(at your option) any later version.
