0
votes
0answers
12 views

Forensics in wireless networks, anomaly detection and beyond?

first i'de like to apologize if this is not the right place. Next year i'm gonna be working on my final project in computer security, i have to build a wireless forensics tool that can analyse a data ...
0
votes
1answer
24 views

How to : a brief intro to scaling and rescaling data ( inputs) for supervised learning algorithms

I understand the concept of scaling and that it improves results in SVM's and NN's. however I would like to find somewhere where is is explained, in easy "layman's terms" terms. of how it is done. I ...
1
vote
0answers
22 views

Bayesian Perceptron - how can I generate many different perceptrons?

I am going to implement the Bayesian version of a perceptron that I read in the Statistical Mechanics of learning, by Engel-Van Den Broeck. The idea to improve the performance is to use many Gibbs ...
1
vote
1answer
50 views

Image processing with neural network

I am trying to learn how Neural Network works on image recognition.I am confused that how neural network that how i will give input.My defination is find(track) object in squence of images(particular ...
0
votes
1answer
32 views

Question about normalize/scale data before using neuralnet

I have read several threads about the issue on same outputs after people fitting a neural network model with R neuralnet. Posted Solution is to normalize or scale the data before fitting model. Since ...
0
votes
0answers
18 views

How Sensitive Are Neural Networks?

CrossPost: https://stackoverflow.com/questions/24301472/how-sensitive-are-ff-neural-networks I am aware of pruning, and am not sure if it removes the actual neuron or makes its weight zero, but I am ...
0
votes
0answers
17 views

Good way to use adaptive learning rates in neural network

Adaptive learning rates means using different learning rate for different weight in neural network. Except for the emperical method which updates these learning rates based on consistency in gradient, ...
0
votes
0answers
19 views

Sparse ELM vs SVM

What's the difference between SVM and Sparse Extreme Learning Machine with Gaussian kernel proposed in the following paper:http://www.ntu.edu.sg/home/egbhuang/pdf/Sparse-ELM-IEEE-T-Cybernetics.pdf As ...
2
votes
1answer
39 views

Having a Neural Network recreate what it's learned

I've created a basic Neural Network that learns from basic information and can verify whether or not a piece of information matches it's parameters from a match percentage. Conceptually however, I ...
0
votes
0answers
13 views

LMS cost function vs cross entropy cost function in neural networks

What is difference between using various cost functions: LMS,Cross entropy in neural networks? All of them have same derivative w.r.t final activation and hence all the gradients are still gonna ...
0
votes
1answer
89 views

tanh activation function vs sigmoid activation function

tanh activation function is nothing but 2*sigmoid - 1. Does it really matter between using those two activation functions. Which function is better in which cases?
0
votes
0answers
19 views

Time and space complexity of Deep Belief Nets (DBN)

What is the time and space(memory) complexity of DBNs? given d:number of dimensions(attributes), n:number of records, and l:number of hidden layers.
0
votes
0answers
24 views

Supervised classification on different time series

I have 300 files, each file has a time series data with a class label(0 or1) for each data point.I want to build a classifier, which can predict the class of a new time series data. How should I ...
0
votes
1answer
33 views

neural network output layer for binary classification

I'm using a neural network for a binary classification problem. Is it better to have one neuron in the output layer or to use two, i.e. one for each class?
0
votes
0answers
24 views

Introduction to recurrent neural networks?

I have two questions: 1- What are the applications of recurrent neural networks? 2- Can you recommend some good resources/papers that introduce recurrent neural networks?
4
votes
1answer
57 views

Where can I find an implementation of Hinton's original Boltzmann Machine?

I've been trying to implement the Boltzmann machine 4-2-4 encoder that appeared in A Learning Algorithm for Boltzmann Machines. but I am unable to find clear pseudocode for doing it or more specific ...
1
vote
1answer
58 views

Trouble training Neural Network

I'm trying to use Encog to define an artificial neural network in order to process this dataset (6 inputs, 2 yes/no outputs), but I can't get any lower than ~65% error. The NN is feedforward with ...
0
votes
1answer
77 views

Explanation of the Regression Plot in the Matlab Neural Network Toolbox

What does the Regression Plot in the Matlab Neural Network Toolbox show? I thought I understood it when I looked at a univariate regression plot, but I've just plotted one for multivariate regression, ...
0
votes
0answers
16 views

Why should the feature be standardized before feeding to the neural network algorithm [duplicate]

Before feeding the features to the neural network algorithm, we have to standardize these features. Why? This is an interview question asked in my recent interview for a data scientist. Can ...
1
vote
0answers
36 views

How should I handle variables whose data points have varying degrees of predictive power?

I'm trying to determine which type of learning algorithm is best for making predictions on my data. My data set consists of several independent variables, each of which is accompanied by an ...
0
votes
0answers
28 views

Is there a neural network r package for mixed model?

R neural network package such as nnet does not allow to specify random variables. I have a dataset with repeated measures of the same subject, which introduce random effects as in a general linear ...
0
votes
0answers
42 views

3D space learning and prediction

I want suggestions about learning and predicting some object's position before hitting one out of four sides of a wall. I have some priority according to side of wall, and of course all the scenarios ...
5
votes
3answers
194 views

What are alternatives of Gradient Descent?

Gradient Descent has a problem of getting stuck in Local Minima. We need to run gradient descent exponential times in order to find global minima. Can anybody tell me about any alternatives of ...
0
votes
1answer
25 views

Neural Network Process Question - Updating weights after each training set

When creating a neural network, do I update the weights after each run of forward then back propogation? Or do I just keep the random weights and update the Delta variables? I am looking at slide 8 ...
0
votes
0answers
27 views

Neural Networks - Calculating delta in Backpropogation

I'm developing software to create a neural network. I have the forward propogation code done, but when I started working on the algorithm for back-propogation I ran into a problem. I'm having ...
1
vote
1answer
35 views

What are problems of many hidden layers?

Is there any problem if we use too many hidden layers in Neural Network? Can anyone simply describe what problems can occur if we have too many hidden layers.
0
votes
1answer
99 views

L1-norm cost function for Neural Network. (Regression)

I am trying to build a regression model using a neural network. The final cost measure is the mean absolute error (MAE) on the output (one output unit, 200 input units). Right now all my hidden units ...
2
votes
1answer
47 views

Neural Network: What if there are multiple right answers for a given set of inputs?

For a given input into the input nodes, there are multiple correct values for the output nodes. In the training set, there are times when the inputs result in a certain output, and other times when ...
0
votes
3answers
111 views

Neural network packages which allow shared weights and parallel training

I'm curious if there are any neural network packages out there that easily allow one to construct feed forward neural networks with shared weights, but also allow for the training to be done in ...
1
vote
1answer
56 views

Computational Complexity of Prediction using SVM and NN?

I've seen answers discussing the complexity of training SVMs and neural nets, but how about for predicting new responses once a model has been trained? For context, I'm working on an app that should ...
2
votes
1answer
172 views

Difference between Bayes network, neural network, Petri Nets and decision tree

What is the difference between Neural network, Bayesian network, Decision tree and Petri Nets eventhough they are all graphical models and visually depict cause-effect relationship. Thank you
1
vote
0answers
30 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
76 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
0answers
44 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
39 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
61 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 ...
6
votes
2answers
601 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
17 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)? ...
0
votes
0answers
24 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
0answers
49 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
79 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 ...
1
vote
1answer
54 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 $K(K'(x))$ ...
3
votes
1answer
97 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
39 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
96 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 ...
1
vote
1answer
61 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
37 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
213 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 ...
0
votes
1answer
27 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 ...
4
votes
1answer
181 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 ...