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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advices about train set and validation set

I know that there should be three sets of tests for supervised learning that are: train validation test I have read that for example in the case of NN in the train phase one chooses the weights ...
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1answer
10 views

Is my interpretation of the numerical gradient versus network output correct?

I have implemented a neural network with back propagation using a sigmoid activation function. To validate the functionality of my code, I am estimating the gradient of my function using the ...
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1answer
33 views

What is obtained from the product of a probability and a log probability ratio?

I'm looking at the commonly used artificial neural network model that has nodes and connections. Quick refresher A connection has a source and target node, and a weight. The output of the source ...
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12 views

Good test error and bad output,Why? [on hold]

I've a problem with my neural network. I use Matlab fitnet with trainlm and validation check and without pre and post processing data. The test error is very good and so I imagine the output of new ...
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1answer
39 views

What is the mathematical underpinning of feedforward artificial neural network?

For a school project, I have implemented a 3 layer feedforward ANN with an RBF activation function that can be used to distinguish between different types of signals. I have a demonstration coming up ...
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20 views

normalizing data for neural network

I'm working on a neural network with back propagation for indoor localization. The input of the neural network is Received Signal Strengths (RSSs) and the output is a coordinate (x,y). I have ...
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0answers
12 views

How to define the maximum number of hidden neurons in neural networks? [closed]

I have 94 observations. How to define the maximum number of hidden neurons in neural networks?
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0answers
31 views

trouble with understanding neural network

Can anyone give an explanation for a page 422 from the The Elements of Statistical Learning. I couldn't understand the meaning of 'the least constrained model.' Paragraph and picture is shown as ...
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1answer
24 views

Prediction vs. Classification in neural networks

I am learning the backpropagtion algorithm, and would like to clarify some concepts. Suppose my training data set consists of 20-dimensional bit strings that are classified into 5 different classes. ...
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1answer
27 views

How to make a trained neural network “forget” an instance?

I am using neural networks for predicting the behavior of a dynamic system. A neural network is trained online using snapshots from the system's past. The system changes its state at irregular ...
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17 views

artificial neural network [closed]

i am dealing in case which involves soil liquefaction problem.I have five inputs and one output.what is the algo to develope relationship between inputs and output somewhat like y=f(a,b,c,d,e) so that ...
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1answer
16 views

Contradictory input/output pairs when training neural network?

I think this question can probably be generalized away from neural networks, bu there goes: How should we handle possibly contradictory data? Suppose the neural networks maps n-bit strings to a bit. ...
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34 views

using Cross Validation in matlab with neural networks

I want to make a cross validation on neural network, I tried to pass the labels to crossval function, with the help of ...
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0answers
13 views

Meaning of feature map in convolutional neural network [closed]

In understanding convolutional neural networks I am not able to visualize what the author means by feature map?
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14 views

Feed-Forward Neural Networks Query

Is there a way to generate and ideal input vectors given an observed output vector in a trained network. In a lot of Autoencoder tutorials it is shown how to visualize 1 unit. Can this be extended to ...
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2answers
51 views

tanh vs. sigmoid in neural net

I apologize in advance for the fact that I'm still coming up to speed on this. I'm trying to understand the pros and cons of using tanh (map -1 to 1) vs. sigmoid (map 0 to 1) for my neuron activation ...
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3answers
105 views

(Feed-Forward) Neural Networks keep converging to mean

I'm having an interesting dilemma with the neuralnet and nnet packages in R. I recently ...
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1answer
11 views

LSTM forgetting dependencies

How does a LSTM network know when is a good time to forget the dependencies it has learned? I understand that it forgets when the value of forget gate is close to zero. But how does it know when to ...
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0answers
19 views

Trying to impelement IRPROP+

I'm stuck I tried 3 times to setup IPROP+. I figured IPROP+ was the most highly rated of the three found here http://heatonresearch.com/wiki/RPROP Problem is... my training doesn't seem to work. ...
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1answer
32 views

Using Adaptive Linear Neurons (Adalines) and Perceptrons for 0-1 class problems

I am wondering how to adjust the Adaline algorithm to classify the classes 0 and 1 instead of -1 and 1. I found a section in Neural Networks and Statistical Learning by Ke-Lin Du, M. N. S. Swamy ...
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1answer
114 views

Deep neural nets, RELU's removing non-linearity?

are RELU (Rectified Linear Units) activation functions considered non-linear? They are linear when the input is > 0 and from my understanding to unlock the representative power of deep networks ...
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31 views

The accuracy measure of a classification process

I have build the signal processing and feature extraction models, those features, inputed to Neural Network using matlab, which is give me the following performance measure,And I have several ...
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1answer
59 views

What are the advantages of using a Bayesian neural network

Recently I read some papers about the Bayesian neural network (BNN) [Neal, 1992], [Neal, 2012], which gives a probability relation between the input and output in a neural network. Training such a ...
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0answers
33 views

Are there ways to improve Levenberg-Marquardt backpropogation performance in Neural Networks?

When using Levenberg-Marquardt optimization for a function approximation problem, the performance and speed generally trumps that of the gradient descent. I am approximating the functions cos(n * Pi) ...
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1answer
40 views

Neural network for time series forecasting- Single input Single output Theoretical proof needed

I am doing time series forecasting using neural networks. I have 2 approaches: Forecasting in a auto regressive manner i.e based on time series lags as shown below: ...
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0answers
22 views

Hyper parameters optimization

Any one with a tip on how to define a suitable condition to find an optimal set of parameters for a combined norm regularization on a grid? Not cross validation or any related method. I am asking if ...
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2answers
25 views

Recommended/estimated number of radial basis functions in RBFN

thank you for taking the time to read my question. I am attempting to make a Radial Basis Function Network to see if a relationship exists between input/output data that I have been collecting. I ...
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0answers
19 views

How to pretrain Convolution filter

I was implementing convolutional neural network, For classification of natural images like face, car, flower etc of about 10 categories. I read(from Andrew NG notes) that pre trained convolutional ...
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1answer
15 views

How to train a supervised RBM for classification?

Typically one uses a RBM in an unsupervised fashion. But it is stated that we can do otherwise. As the title says how does one train a supervised RBM? My idea is the following: by clamping feature ...
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2answers
72 views

Why don't we train neural networks to maximize linear correlation instead of error?

Recently a project I've been a part of has involved training neural networks so that we maximize the Pearson correlation between actual and predicted values. So this came to my mind: why don't we ...
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0answers
20 views

Entropy, Softmax and the derivative term in Backpropagation

I'm currently interested in using Cross Entropy Error when performing the BackPropagation algorithm for classification, where I use the Softmax Activation Function in my output layer. From what I ...
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1answer
50 views

How large should the sample be for stochastic gradient descent?

I understand that stochastic gradient descent may be used to optimize a neural network using backpropagation by updating each iteration with a different sample of the training dataset. How large ...
1
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0answers
33 views

Which method to use for load forecasting

I have smart meter data set that has consumption readings collected over a year and a half for every 30 mins. What I am trying to do is short term load forecasting. The data set has just three columns ...
1
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1answer
37 views

What is the best lag length for auto correlation?

I am doing a monthly rainfall forecasting model. I have monthly data from 1998 to 2012. I found in previous research that they have used partial autocorrelations and stepwise regression as an input ...
1
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1answer
28 views

What are the differences between filters learned in autoencoder and convolutional neural network?

In CNN, we will learn filters to produce feature map in convolutional layer. In Autoencoder, each layer's single hidden unit can be considered as filter. What the difference between the filters ...
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0answers
7 views

What is an intuitive explanation of Echo State Networks?

A reference can be found here: http://www.scholarpedia.org/article/Echo_state_network I am new to Recurrent Neural Networks(RNN) and still learning the concepts. I understand at an abstract level ...
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13 views

Output's probabilities of ANFIS in MATLAB

Is anyway to have output probabilities of binary classification using MATLAB ANFIS toolbox (or other related toolboxes)? Outputs are 1 and 0. So if we have 0.7 for ...
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0answers
14 views

When does weight sharing in RNN's make sense (or not)?

It is my understanding that RNN's share weights. It seems to me that this may not be wise for all situations. So if you use an RNN (with weight-sharing) what are you assuming about the problem you are ...
0
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1answer
14 views

R's nnet needs decay to perform with sin() like function, why variant reproducibility

I've noticed that for sin() like data, I need to use "decay" which is available in nnet to get the ANN to perform. Why would that be in theory? Also when I run runNN(0.02) over and over, sometimes ...
0
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1answer
22 views

Question about Continuous Bag of Words

I'm having trouble understanding this sentence: ...
1
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2answers
60 views

Feature selection before neural network classification

I have a training set of 87 samples and 9480 variables. My predictors are continuous and my response variable is binary. I'd like to use the caret package in R to tune a neural network classification ...
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0answers
19 views

Sensitivity analysis of machine learning techniques

As you know we can have sensitivity analysis (sensitivity of output(s) based on changing of inputs) in different kinds of regression. Can we have sensitivity analysis for machine learning techniques ...
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0answers
19 views

How to select topology for neural network? [duplicate]

I was given a target function to design neural network and train: (y = (x1 ∧ x2) ∨ (x3 ∧ x4)) The number of input and number of output seems obvious (4 and 1). And the training data can use truth ...
8
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2answers
413 views

Timeseries analysis procedure and methods using R

I am working on a small project where we are trying to predict the prices of commodities (Oil, Aluminium, Tin, etc.) for the next 6 months. I have 12 such variables to predict and I have data from ...
0
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0answers
7 views

Can the rectangular function be used to filter out magnitudes in logistic regression to add more flexibility?

I would like to use logistic regression rather than an artificial neural network to be able to more easily interpret the results. I would like though to be able remove the linearity by introducing a ...
2
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1answer
38 views

Classification when some classes are dependent

I think my problem can easier be explained via an example: Assume we have a dataset containing the images of 10 different mammals, let's say lion, elephant, cat, ... and horse. We have a 20-class ...
1
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2answers
135 views

How can an artificial neural network ANN, be used for unsupervised clustering?

I understand how an artificial neural network (ANN), can be trained in a supervised manner using backpropogation to improve the fitting by decreasing the error in ...
0
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2answers
22 views

Neural networks: Active input range of activation functions?

I'm playing with the Neural Network toolbox in MATLAB. I've noted that each activation function (aka, transfer function) has 2 properties: the output range, which, if I understand, is the codomain ...
0
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0answers
31 views

How to set up neural network to output ordinal data?

I have a neural network set up to predict something where the output variable is ordinal. I will describe below using three possible outputs A < B < C. It is pretty obvious how to use a ...
1
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1answer
19 views

How to find $\arg\max$ of a neural network?

Let's say I have a neural network $f$ that takes input $\vec x \in \mathbb {R}^n$ and produces output $f(\vec x) \in \mathbb{R}$. How can I find $\hat x = \underset{\vec x}{\arg\max} \; f(\vec x)$?