Neural networks traditionally refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks (ANN), which are composed of artificial neurons or nodes - programming constructs that mimic the properties of biological neurons. ANN are ...
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21 views
Data normalization before giving to Neural Nets or Deep Learning algorithm?
What kind of normalization scheman is required for the best of NN algorithms? I saw some people just give the data to signum function before passing to NN and some of those process data by regular ...
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31 views
Loss Matrix Equivalent with Neural Networks and random Forest
I'm doing classification (0,1) on a dataset for which different types of errors should be weighted differently. IE, false positives would be weighted 10 x more than false negatives.
In decision ...
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26 views
Activation value at output neuron equals 1, and the network doesn't learn anything
I'm implementing a typical neural network with 1 hidden layer. The network does well with the logic XOR and other simple problems, but fails miserably when encountering a (16-input, 20~30 hidden, 3 ...
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25 views
matlab neural network strange simulation performance
I am not quite sure if this is the right place for a question like this, but asking anyway.
Having <14x10 double> input matrix (manually normalized) and <5x10 double> output matrix (manually ...
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1answer
87 views
Invariance in neural networks
I have tried to read about tangent propagation in neural networks (although I guess it could be applicable to other methods) which is a procedure to create models that are invariant to certain ...
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3answers
126 views
What does “degree of freedom” mean in neural networks?
In Bishop's book "Pattern Classification and Machine Learning", it describes a technique for regularization in the context of neural networks. However, I don't understand a paragraph describing that ...
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2answers
36 views
Neural network with skip-layer connections
I am interested in regression with neural networks.
Neural networks with zero hidden nodes + skip-layer connections are linear models.
What about the same neural nets but with hidden nodes ?
I am ...
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39 views
trouble in prediction in neural network classifier
I am training a 4-class neural network classifier.
The details of my data are:
featurelength = 280
...
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1answer
39 views
R - how can I use neural networks for a binary dependent variable in R?
I have a dataset from a bank with demographic data and one variable telling if the customer is a good customer or not (binary variable). I would like to do prediction on if the customer is good or not ...
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1answer
42 views
Averaging weights learned during backpropogation
Is it possible to train a neural network N times, using backpropogation and then average the weights learned to produce a more accurate classifier?
My tests are indicating no, but I'm unsure if it is ...
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3answers
83 views
Backpropagation vs Genetic Algorithm for Neural Network training
I've read a few papers discussing pros and cons of each method, some arguing that GA doesn't give any improvement in finding the optimal solution while others show that it is more effective. It seems ...
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2answers
100 views
Is ARIMA better in comparision with Neural Networks?
After working on Backpropagation Neural Network and ARIMA Time Series Model, I asked myself which one is better, but can't figure out the answer. They both use different approaches on the same problem ...
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2answers
75 views
ML with fastest classification speed
I have a data classification problem and I'm wondering what is the best machine learning approach to use for the particular constraints of my problem.
My constraints are as follows:
- the data ...
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25 views
Decimal places in Summary(model) output in R [migrated]
I am trying to get more than 2 decimal places from model summary output when I use nnet package. I read other threads regarding this and none of those solutions ...
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20 views
Time delay estimation for non linear physical signals
I have a non linear physical system with 4 Inputs and 2 Outputs and I want to model it using a time series model such as NARX model. Because I'm new in system modeling and signal processing I do not ...