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 ...

learn more… | top users | synonyms

1
vote
0answers
4 views

Differencebetween Bayes network and Neuro fuzzy

Referring to this answer Difference between Bayesian network and neural network, I have come across another graphical model (1) Fuzzy Cognitive Map and (2) Neuro-Fuzzy. Bayesian Network (BN), Fuzzy ...
1
vote
0answers
17 views

How to derive errors in neural network with the backpropagation algorithm?

From this video by Andrew Ng around 5:00 How are $\delta_3$ and $\delta_2$ derived? In fact, what does $\delta_3$ even mean? $\delta_4$ is got by comparing to y, no such comparison is possible ...
-1
votes
1answer
21 views

Is it OK to increase validation checks and decrease min gradient while training neural network?

My input vector is a 130*85 matrix and my target vector is 130*26 matrix. I am using the below parameters for training the network with 60 hidden nodes. ...
0
votes
1answer
24 views

if technical analysis rules for predict stock prices is unique for all cases, why should we learn neural networks? [on hold]

Is there any neural network out of the box tool that was already learned all technical rules by feeding many stock trading data?
0
votes
0answers
16 views

Tutorial on Radial Basis Function Networks?

I want to learn about Radial Basis Function Neural Networks, can you please suggest a good introduction or tutorial? All the introductions I found are rather short or incomplete or so.
0
votes
1answer
23 views

Decision boundary equation of the perceptron

As I know the standard linear equation has the following form in $R^2$: $w_1 x_1 + w_2 x_2 = b$ where $b$ is the intercept with $x_2$ Also I know that a decision boundary in $R^2$ for a perceptron ...
2
votes
1answer
41 views

Gradient decay in neural networks

I read that in traditional feed-forward neural nets the gradients in the early layers decay very quickly and that this is 'a bad thing'. But I don't understand why. Can someone please explain what ...
0
votes
1answer
29 views

Ways to simplify a neural network in R for interpretation

I would like to try to make some sense of a neural network. The neural network has a single hidden layer and is used on 30-40 attributes, which are used to classify the probability that the ...
6
votes
2answers
561 views

Meaning of a neural network as a black-box?

I often hear people talking about neural networks as something as a black-box that you don't understand what it does or what they mean. I actually I can't understand what they mean by that! If you ...
0
votes
0answers
10 views

Why feature maps are indexed by two indices?

I'm reading about convolutional neural networks. As I understood a feature map is a set of neurons (i.e like a single hidden layer in traditional ANN). So why feature maps are indexed by (i,j)? ...
4
votes
2answers
202 views

Why is logistic regression a linear classifier?

Since we are using the logistic function to transform a linear combination of the input into a non-linear output, how can logistic regression be considered a linear classifier? Linear regression is ...
1
vote
1answer
11 views

Is it possible prediction of chemical activity with few data?

I have activity data (represented by a real number) for five chemical compounds (and for which I have a set of 600 descriptors) and would like to use neural networks or SVM or any other system that ...
1
vote
0answers
25 views

Outlier detection in binary classification

I have a question about outlier detection in my system. I’m designing a system (in Matlab) that optimize both features and parameters of a classification method (like mlp) together with optimization ...
2
votes
0answers
40 views

Conceptual issues in training neural network and learning curve

I have a 4 Input and 3 Output Neural network trained by particle swarm optimization (PSO) with Mean square error (MSE) as the fitness function using the IRIS Database provided by MATLAB. The fitness ...
0
votes
0answers
15 views

Help about a perceptron question

while studying for my Machine Learning exam, I encountered a problem that I cannot understand. In the problem, we have this perceptron, which 3 binary inputs (0 or 1) a,b,c with respective weights of ...
1
vote
1answer
33 views

K-fold cross validation for neural networks: separate validation set also needed?

When using k-fold cross validation in a neural network, do you also need a separate validation set? Or is the use of the k-fold on its own good enough to minimise the possibility of over-training?
1
vote
0answers
33 views

Why is the default cost function choice of a neuron quadratic loss?

I'm studying neural networks, and I'm trying to decide why the default choice of cost function for a single neuron seems to be quadratic loss: $$\sum_i(y_i-f_i)^2,$$ instead of: ...
1
vote
1answer
34 views

Why features compression is good?

I'm reading about deep learning and that in principles it's a features compression technique and that is why it works. Now my question is why compressing features from 200 or so into 4 is better? How ...
0
votes
0answers
16 views

Backpropagation for classification - finding a rigorous proof

I'm currently learning neural networks and I have a hard time to find a proper proof why the derivative of the cost function can be computed via backpropagation for (binary) classification. In ...
1
vote
0answers
30 views

Neural net model - error during training

I'm getting started with R, I really like it but recently I found myself in a corner. I'd like to build neural network model that predicts heat consumption. I have historical data that contains ...
0
votes
0answers
27 views

What is the best algorithm for finding attacks from log file [closed]

I m working on forensic analysis of web logs. I have generated the DoS attack dataset and i m having the attack dataset of log files (unlabeled dataset) taken from Dr. Anton Chuvakin. I need to look ...
1
vote
1answer
46 views

Cascade Combination of Kernel Functions

I have a question regarding machine learning and specifically kernel functions. Suppose we have a Kernel function, say $K(x)$, and also another distinct one, say $K'(x)$. I want to know is $K(K'(x))$ ...
0
votes
0answers
16 views

When Restricted Boltzmann Machine “dreams” how do you sample the results?

I've tried to sample by analyzing single step of the "dreaming" but the output seems nonsensical. There are some patterns but they don't really look like the data the RBM was shown ( for example, ...
2
votes
0answers
30 views

Neural Network Error Plot Odd Effect

I'm using R to fit a neural network to data generated by the formula $y = x^2 + \epsilon / 2$ where $x \sim \mathcal{U}(0, 2)$ and $\epsilon \sim N(0, 1)$ (very simple, right?). The following plot ...
0
votes
0answers
17 views

Nonlinear problem feature selection

I am trying to make prediction of students course score with neural networks. I have lots of parameters may affect to one course score like sex, GPA and many previous course scores. Before trying to ...
0
votes
0answers
11 views

Measuring the standard deviation in Pattern recognition

Sometimes in pattern recognition say Character recognition, Hamming distance is used although there are other distance measures. But if the pattern is represented in (1,0,-1) then Hamming distance is ...
0
votes
0answers
25 views

Scaling/Normalisation or Standardization

I'm working on SVM and ANN classification tools. In order to improve the classification accuracy, I want to know the best or the recommended data-preprocessing, is it scaling/normalisation or ...
0
votes
0answers
16 views

Difficulties in applying activation function in neural network

This is beginner level question. I have several training inputs in binary and for the neural network I am using a sigmoid thresholding function ...
2
votes
1answer
26 views

Can a deep belief network (stacked RBMS) be used solely as a dataset generator?

I have a large dataset (tens of thousands of predictors) on which I would like to perform feature reduction with the intent of better model-building for prediction. Deep Belief Networks seem to ...
0
votes
1answer
23 views

Offline training or batch wise training

Can somebody please explain how to train a neural network in batch mode. I have a single target time series of length $N$ for a given input time series of the same length. In order to apply Hopfield ...
0
votes
1answer
41 views

Issue in training Hopfield network and convergence problem

I am learning how to use Hopfield Neural network as a context addressable memory. The objective is to obtain a fixed point of the network which indicates an equilibrium state. This state vector ...
0
votes
0answers
13 views

What are the advantages of using a Neural Network to impute data?

What are the advantages and drawbacks of using neural network methods to impute data? Is the bias and total error any higher than other methods (e.g., median or mean method, nearest neighborhood, or ...
1
vote
1answer
52 views

What kind of model is used by 20 Questions?

Which kind of machine learning concept / model is used in 20 Questions? Is this kind of thing best solved by a neural network? Where I can read something about it?
0
votes
0answers
35 views

Neural Network, dependence among outputs?

Is there a way to train a neural network in the following manner: You have $n$ observations in the training set. The neural net will start with random weights, and produce $n$ outputs. I want to ...
1
vote
1answer
115 views

How to design neural networks for pattern recognition in biometry?

Having read numerous texts regarding neural networks and their characteristics, I am getting increasingly confused, paradoxically – I am looking for a brief explanation or references to the right ...
2
votes
1answer
18 views

Interpretation of logloss value

Does anyone have a interpretation of a logloss value? Am I correct to assume that values closer to 0 and 1 are more likely to be an indication that the predicted value is incorrect? Thanks.
0
votes
0answers
30 views

Back propagation in Convolutional neural networks

I am trying to understand how CNN works. I want to use them in object recognition task. I thouhgt that CNN is unsupervised networks. My main question is how can I implement the back propagation ...
0
votes
1answer
89 views

R time-series forecasting with neural network, auto.arima and ets

I've heard a bit about using neural networks to forecast time series. How can I compare, which method for forecasting my time-series (daily retail data) is better: auto.arima(x), ets(x) or ...
0
votes
1answer
18 views

highly sporadic validation error during training with multilayer perceptron

I'm encountering an issue where a classifier I'm developing reports validation errors during training that span a wide range of values without consistently decreasing over time. Unfortunately, I'm new ...
0
votes
1answer
17 views

Neural Networks output range in simulation

I am learning some model based on examples ${((x_{i1},x_{i2},....,x_{ip}),y_i)}_{i=1...N}$ using a neural network of Feed Forward Multilayer Perceptron (newff) (using python library neurolab). I ...
0
votes
0answers
26 views

Neural networks in multi-class setting: How to train/test with cross-validtion; how to evaluate?

I am currently working on a project where neural networks are used for email categorization. There are two things where I still have some problems understanding the general approach: cross-validation ...
4
votes
1answer
131 views

Bayesian MLPs using the MCMC methods - any tricks of the trade?

Having used the NETLAB library for MATLAB to implement Bayesian Multi-Layer Perceptron (MLP) neural networks using MacKay's evidence framework, I am now experimenting with Markov Chain Monte Carlo ...
1
vote
1answer
36 views

How to initiate bias node in a restricted Boltzman machine

I am new to Neural Networks and trying to implement RBM. I am stuck on initializing the visible layer's bias value. Is it supposed to initialize to some random number or there is some probabilistic ...
0
votes
0answers
34 views

Supervised or unsupervised learning problem?

currently I'm working a pattern recognition problem. I have been using supervised learning (neural network and svm with one class classification) but I think I'm doing it in a wrong way. For ...
1
vote
1answer
27 views

Limit multiclassification SVM - ANN

I have some questions on the limits of SVM and ANN for multiclass problem. I know about "one vs all" and "all vs all" strategies but I only want to know the limit of a unique SVM and ANN. Is there a ...
0
votes
1answer
24 views

Supervised learning based on phase space representation

Phase space learning Paper1 and Paper2 in neural network represents the input in higher dimension in auto associative learning. So, the network functions as an auto-associative memory where dynamical ...
1
vote
0answers
34 views

Implement neural network to perform multivariate poisson regression with implied rates

I am now looking at implementing a neural network that will take in 4 input variables, and will output 24 variables. All of the output variables are related (in more than one way) to each other so I ...
0
votes
1answer
47 views

Help requested with using custom model in caret() package

The caret package (terrific btw) has a lot of models built in but if you want to use a model that is not built in, there is a way as described in outline here ...
2
votes
1answer
61 views

Evaluating the clustering of a Kohonen UMatrix

Given a converged Kohonen feature map, how would one evaluate the clustering in terms of intra- and inter-cluster distances? Assuming that both the trained codebook vectors and Unified Distance ...
0
votes
1answer
29 views

Moving from support vector machine to neural network (Back propagation)

I'm working with text recognition and currently I'm using support vector machine method. I would like to try with neural network also. I read a few documents about how neural network works, but the ...