Network structure inspired by simplified models of biological neurons (brain cells). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof.

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2answers
44 views

Perceptron learning rule for classification

That's the problem $$y=(x,w,\rho) = \begin{cases} 1 & \sum_{i=1}^3 w_ix_i >\rho\\ 0 & \text{otherwise} \end{cases},$$ where $x=\{x_1,x_2,x_3\}$ are inputs, $w=\{w_1,w_2,w_3\}$ are ...
1
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2answers
53 views

Perceptron learning rule doesn't work [on hold]

i'm a little bit lost ... can you help me ? So I have this table of date (each row give a point with its group) So i took a random weight let's say : [1, -2] H = 1 if n =< 0 0 otherwise ...
1
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0answers
14 views

What types of images should I use for negative examples in a classification problem? [duplicate]

I am doing a project to recognize a kind of leaf using ANNs with Emgu CV in C#. My project is to get frames from camera then present them to the ANN and have the ANN tell me if that frame contain a ...
1
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1answer
50 views

What are good counter-examples when training an apple classifier?

I am doing a project in order to recognize an apple. (I am using Emgucv with Visual Studio 2010 C#, if that's relevant). My project is a classification (is or is not an apple). I have 2000 images of ...
2
votes
1answer
20 views

How can an artificial neural net change the sign of a weight?

My neural net is having trouble switching the sign of a weight. The issue is that the deltas applied to the weight are proportional to that weight, so when it gets closer to zero, the deltas become ...
2
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2answers
39 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?
2
votes
1answer
36 views

Neural network: noisy temporal sequence converter (transducer?producer?) on demand?

I start to suspect this problem is very hard now that I cannot find a single relevant literature on the subject, but it's too late to change the class project topics now, so I hope any pointers to a ...
0
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0answers
12 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 ...
3
votes
1answer
160 views

Does there exist a data compression algorithm that uses a large dataset distributed with the encoder/decoder?

If my goal were to compress say 10,000 images and I could include a dictionary or some sort of common database that the compressed data for each image would reference, could I use a large dictionary ...
-1
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1answer
45 views

In back propagation why is this necessary, o (1 - o) [duplicate]

To calculate the error in back propagation you would use, (target_output - actual_output) * actual_output * (1 - actual_output) So what does, actual_output * (1 - actual_output) solve? Wouldn't, ...
1
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1answer
44 views

What's the purpose of the “o(1-o)” in the back propagation algorithm

I'm not sure what the purpose of the o(1-o) in the back propagation algorithm achieves? I'm guessing it's related to using the sigmoid function on the output but I'd like to have a proper ...
1
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1answer
48 views

How are Bayesian Nets, Hidden Markov Chains, Conditional Random Fields and Neural Nets related?

I am having an AI exam in two weeks, and I am still figuring out certain concepts and ideas, related to Bayesian Nets, Hidden Markov Chains, Conditional Random Fields and Neural Nets (yes it is all ...
8
votes
3answers
24 views

When should I move beyond k nearest neighbour

For many machine learning projects that we do, we start with the k Nearest Neighbour classifier. This is an ideal starting classifier as we usually have sufficient time to calculate all distances and ...
3
votes
1answer
65 views

What are the key differences between Spiking Neural Network and Deep Learning

Deep Learning, now one of the most popular fields in Artificial Neural Network, has shown great promise in terms of its accuracies on data sets. How does it compare to Spiking Neural Network. Recently ...
2
votes
1answer
50 views

Neural Network Normalization and de-Normalisation of data

I am developing a simple backprop neural network with n inputs and 1 output. I am using a sigmoid activation function. [Aforge.Net] I have read that it is good to normalise the input and output data ...
0
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0answers
24 views

Support Vector Machines as Neural Nets?

This is more of a conceptual question. I have learned about Neural Nets, and I have some clue as to how Support Vector Machines work. I read somewhere however that given the appropriate kernel (is ...
1
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1answer
35 views

Multi-dimensional Neural Network for fingerprint matching

I want to use “Fingerprint matching using multi-dimensional ANN” by Rajesh Kumar and B.R. Deva Vikram [content link] for fingerprint identification. But I have a serious problem understanding what is ...
0
votes
1answer
80 views

How to make a Neural network understand that multiple inputs are related to the same entity?

Neural networks can have multiple inputs. But some times two or more of these inputs can often be related to a single entity. E.g : Height and weight of a person to predict the probability of disease ...
1
vote
1answer
51 views

Making feature vector from Gabor filters for classification

My aim is to classify types of cars (Sedans,SUV,Hatchbacks) and earlier I was using corner features for classification but it didn't work out very well so now I am trying Gabor features. code from ...
8
votes
1answer
366 views

What is the difference between a Neural Network, a Deep Learning System and a Deep Belief System?

What is the difference between a Neural Network, a Deep Learning System and a Deep Belief System? As I recall your basic neural network is a 3 layers kinda thing, and I have had Deep Belief Systems ...
6
votes
2answers
110 views

Kernelization trick, for neural networks

I've been learning about neural networks and SVMs. The tutorials I've read have emphasized how important kernelization is, for SVMs. Without a kernel function, SVMs are just a linear classifier. ...
3
votes
0answers
43 views

practical use of a Boltzmann machine

I am reading "Neural Networks and Learning Machines" and in Chapter 11 the book covers Boltzman machines and it is stated "the network [Boltzmann machine] can perform pattern completion", but does not ...
3
votes
2answers
184 views

How to encode date as input in neural network?

I am using neural networks to predict a time series. The question I'm facing now is how do I encode date/time/serial no. of each input set as an input to the neural network? Should I use 1 of C ...
0
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0answers
18 views

Fast energy function substitute for Hopfield network?

The energy function in a Hopfield network to determine whether it has converged seems to be the major sink of computational time and makes the Hopfield network run very slowly. Is there a fast ...
6
votes
1answer
108 views

Why are weights of Neural Networks initialized with random numbers?

Why are neural networks initial weights initialized as random numbers? I had read somewhere that this is done to "break the symmetry" and this makes the neural network learn faster. How does breaking ...
4
votes
1answer
103 views

Something I don't understand about Genetic Algorithms

I've had a bit of experience programming Neural networks but I am fairly new with genetic algorithms (I'm only 17). I have a major issue that I can't understand. If a child get's one chromatid from ...
3
votes
1answer
135 views

Neural network diverging instead of converging

I have implemented a neural network (using CUDA) with 2 layers. (2 Neurons per layer). I'm trying to make it learn 2 simple quadratic polynomial functions using backpropagation. But instead of ...
2
votes
1answer
125 views

Derivation of the energy function of a hopfield network

Can someone please point me towards a rigorous derivation of the energy function of a discrete Hopfield network. What I want, is the derivation must start out with the structure of the network and ...
3
votes
1answer
485 views

Some criticisms of Hierarchical Temporal Memory?

I just recently learnt about the existence of this Hierarchical Temporal Memory. I already read the main document (which seems rather easy to understand), but one red flag is that the document is ...
2
votes
0answers
182 views

Intuitive description for training of LSTM (with forget gate/peephole)?

I am a CS undergraduate (but I don't know much about AI though, did not take any courses on it, and definitely nothing about NN until recently) who is about to do a school project in AI, so I pick a ...
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1answer
23 views

How to test and create ANN models?

I am learning artificial neural networks and i am very new to artificial neural networks. How to create artificial neural network programs and test them?, how to make my artificial neural networks ...
3
votes
1answer
55 views

How are two layer feed-forward neural networks universal?

Across my studies I have noticed the following statement in my Subject Guide; namely, that two-layer feed-forward neural networks using the sigmoidal activation function are universal. My question is ...
6
votes
1answer
108 views

Adapting neural network

I have on a few occasions trained neural networks (back propagation networks) with some rather complicated data sets (backgammon positions and OCR). When doing this, it seems that a lot of the work ...
6
votes
2answers
170 views

What can be learned from the weights in a neural network?

I'm very new to neural networks, and have been trying to figure some things out. So, let's say you come across a neural network which has 100 inputs, a hidden layer with 200 nodes, and 32 outputs. ...
2
votes
2answers
144 views

About the behaviour of multi-layer perceptrons

I have a multilayer perceptron. It has an input layer with two neurons, a hidden layer with an arbitrary number of neurons, and an output layer with two neurons. Given that ...
1
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1answer
221 views

Neural Network weight selection using Genetic Algorithm

Hi I want to ask about weight selection in neural network using genetic algorithm. Right now what I understand is Initialize population Encode the weight of the neural network to the chromosome ...
1
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1answer
35 views

What factors must one consider choosing an NN structure?

Suppose we have a classification problem and we wish to solve the problem by Neural Network. What factors must one consider choosing an NN structure? e.g Feed Forward, Recurrent and other available ...
1
vote
2answers
230 views

What's the difference between a rule based system and an artificial neural network?

I'm currently doing some reading into AI and up to this point couldn't find a satisfying answer to this question: what's the difference between a rule based system and an artificial neural network? ...
5
votes
3answers
523 views

What piece am I missing to turn this idea into a programming language?

I've been doing some reading (I'll name drop along the way) and have selected a few scattered ideas that I think could be cobbled together into a nifty esoteric programming language. But I'm having ...
3
votes
1answer
75 views

How to compare the output of a neural network with his target?

I am coding a neural network implementation, but a I have problems in the design. I was wondering about how to compare the output with the target, my neural networks has three outputs ...
8
votes
1answer
515 views

Is there a simple way of calculating perceptron weights based on a classification graph?

I am studying for an AI exam and I'm looking for a better way of solving the following problem: Graph shows a classification problem in the unit square $[0,1]^2$, where Class A is denoted by the ...
3
votes
2answers
96 views

Finding Feature Representation Such That Two Samples Are Similar in Feature Space

Consider one specific useful function of our human brain: abstraction of object. Take the example of two pictures: if we are told the pictures are similar, we actually make conclusion about the ...
5
votes
1answer
118 views

How would a neural network deal with an arbitrary length output?

I've been looking into Recurrent Neural Networks, but I don't understand what the architecture of a neural network would look like when the output length is not necessarily fixed. It seems like most ...
2
votes
1answer
504 views

Could an artificial neural network algorithm be expressed in terms of map-reduce operations?

Could an artificial neural network algorithm be expressed in terms of map-reduce operations? I am also interested more generally in methods of parallelization as applied to ANNs and their application ...
9
votes
2answers
866 views

Must Neural Networks always converge?

Introduction Step One I wrote a standard backpropegating neural network, and to test it, I decided to have it map XOR. It is a 2-2-1 network (with tanh activation function) ...
7
votes
2answers
201 views

Is computational power of Neural networks related to the activation function

It is proven that neural networks with rational weights has the computational power of the Universal Turing Machine Turing computability with Neural Nets. From what I get, it seems that using ...
8
votes
1answer
206 views

How many layers should a neural network have?

Are there any advantages of having more than 2 hidden layers in a Neural Network? I've seen some places that recommend it, others prove that there is no advantage. Which one is right?
11
votes
1answer
204 views

Efficiently computing or approximating the VC-dimension of a neural network

My goal is to solve the following problem, which I have described by its input and output: Input: A directed acyclic graph $G$ with $m$ nodes, $n$ sources, and $1$ sink ($m > n \geq 1$). Output: ...
22
votes
2answers
298 views

Why do neural networks seem to perform better with restrictions placed on their topology?

Fully connected (at least layer to layer with more than 2 hidden layers) backprop networks are universal learners. Unfortunately, they are often slow to learn and tend to over-fit or have awkward ...
7
votes
1answer
126 views

The essential difference between spiking neural networks and earlier generation ANN's

I have been studying Spiking Neural Networks online from various papers, mainly Maass (1997). I am not entirely sure I understand what makes SNN's pulse-code in contrast to earlier ANNs which are ...