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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2
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1answer
24 views

Closed form solution for a single layer linear perceptron

Let f be a one-layer neural network which is linear (ie. no activation function). Let it have $p$ inputs and $q$ outputs. These are fully connected by weights $W$. We have $n$ inputs $x \in ...
0
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0answers
45 views

Where to start studying about HTM?

I am looking for references (pedagogic and beginner friendly!) to these two topics, hierarchical temporal memory algorithms applied to deep planning problems (multi-layer) neural networks trained ...
3
votes
1answer
26 views

Training given pairs of similar values, not labels

I have pairs of "similar" values $(x_i, y_i)$ drawn from a space $x_i, y_i \in S$, and want to train a neural network $N$ such that $N(x_i)$ would be "close" to $N(y_i)$ for all $i$, yet, to make it ...
1
vote
1answer
22 views

Matching training with Neural Network

I have a matching algorithm that is based on making an comparison score. This score is divided into parts. Example: 5 - Points for attributes (lets say they have 3 common attributes, would the score ...
-2
votes
1answer
42 views

Neural Network Design Challenge

i'm studying for PHD Entrance Exam on Stanford. one of previous material exam designed very challenging. i want to design a NN for classifying following 2-class problem. 1) output should be -1 or ...
0
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0answers
29 views

Can anyone help with a simple neural net I am trying to code?

I have a neural net with two input units (and a bias) connected to four hidden units, with each input unit (including the bias) connected to all the hidden units. Then there is one output unit. All ...
0
votes
0answers
8 views

Difficulty in understanding fuzzy logic and fuzzy system in classification

I am applying Neuro fuzzy system for classifiying objects. For example: If object{color} is Green And object{size} is small And object{shape} is Square Then Object is GreenBox. Hence, the Situations ...
2
votes
0answers
55 views

How can you use HMMs and ANNs for on-line handwriting recognition?

On-line handwriting recognition is the task of converting a series of $(x(t),y(t))$ coordinates to symbols and words. In contrast to off-line handwriting recognition, where you only have a bitmap of ...
0
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0answers
28 views

Backpropagation Through Time Recursive Algorithm

Would it be plausible to write a recursive version of backpropagation through time for recurrent neural network training? I've only found the iterative version: ...
4
votes
0answers
25 views

How to choose proper activation functions for hidden and output layers of a perceptron neural network?

As far as I know choosing an activation function for the input layer is relatively straightforward: I use Sigmoid if the input data domain is (0,1) and TANH if it is (-1,1). But what activation ...
2
votes
1answer
31 views

How do I “tell” a simple perceptron/adaline neural netrork its output can't be negative?

I have made and trained a simple neural network which now seems to produce outputs reasonable in all the aspects but one: it gives negative values from time to time even though the outputs are always ...
1
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2answers
63 views

How to determine if a state is a fixed point in a Hopfield network?

I have been reading a lot and I am still unsure of how to determine this. Let's say I have an initial binary state vector (1, 1, 1). How would I go about determining whether (1, 1, 1) is a fixed point ...
-3
votes
2answers
75 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
61 views

Perceptron learning rule doesn't work [closed]

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
vote
1answer
70 views

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

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 ...
4
votes
2answers
44 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
votes
2answers
47 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
55 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 ...
3
votes
1answer
183 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
votes
1answer
47 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
vote
1answer
45 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
82 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
28 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
175 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
65 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
27 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
vote
1answer
39 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
89 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
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1answer
67 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
622 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 ...
5
votes
2answers
157 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
44 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
271 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
20 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
146 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
122 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
170 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 ...
3
votes
2answers
173 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
769 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
202 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 ...
4
votes
1answer
59 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
123 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
200 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
150 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
vote
1answer
247 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
vote
1answer
43 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
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2answers
283 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
569 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
82 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 ...