Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.

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0
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0answers
25 views

clustering algorithm for heterogenous objects [on hold]

Could anyone point out to the algorithm or literature that talk about clustering of complementary objects?  I am interested in algorithm that make cluster of objects that have complementary wind ...
-3
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0answers
15 views

Video algorithm question [closed]

Kindly help me with this question: • A short design document detailing what you and why, which algorithms were used and what methods. • Working code solving the problem, and sample output for the ...
1
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2answers
50 views

How to optimize a function by maximizing 1 variable and minimizing another?

Problem I want to implement an optimization algorithm for my file transfer program. The program buffers data in a local file before uploading to central server periodically and it compresses the ...
5
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0answers
60 views

Formulating shortest path as submodular minimization

I'm curious about the general question of whether any combinatorial optimization problem with polynomial time solution can necessarily be reformulated as minimizing a submodular function. The answer ...
0
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1answer
22 views

What are Predictive Clustering Trees in machine learning?

Anybody could let me know what exactly the PDT is and where does it come from? it comes from predictive modeling or decision tress? I read some articles and websites like: this and this but both of ...
3
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2answers
39 views

What restrictions apply to query and target vector encoding on fast-forward neural networks?

I'm currently studying fast-forward multi-layer neural networks with back propagation, in the book I see that all query and target vectors are binary-encoded, this makes me believe that this is the ...
5
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0answers
56 views

Computer vision: Why do random filters perform similar to edge detectors?

I read here that "a randomly initialized filter acts very much like an edge detector!". I want to know if there are any papers describing and explaining this phenomenon.
3
votes
1answer
19 views

Simple Bayesian classification with Laplace smoothing question

I'm having a hard time getting my head around smoothing, so I've got a very simple question about Laplace/Add-one smoothing based on a toy problem I've been working with. The problem is a simple ...
4
votes
1answer
37 views

Expected number of common edges for a given tree with any other tree

So I am working on a problem where I have a set of (labeled) nodes and I have a tree structure (rooted) over that set of nodes. The goal for me is to automatically generate that tree structure. To ...
-1
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2answers
49 views

Clarification of Generalizing and Overfitting

To get to clarification of "generalizing" and "overfitting" I first must state the following: We have a robot, it makes guesses, and the way we know what guesses to keep is by if the received sensory ...
3
votes
1answer
23 views

Recommender System - Binary Ratings without explicit dislike

I'm currently looking into developing a system in which media from various sources is collected, with the notion that the media collected for each user is media they 'like'. This results in a set of n ...
-1
votes
1answer
43 views

Doesn't this method of Machine Learning do perfectly fine? [closed]

There's many types of Machine Learning methods, but one sticks in my mind, and I don't understand why for ex. Q Learning was made instead of just using the following method: Method: Say we give the ...
21
votes
12answers
7k views

Why is overfitting bad?

I've studied this lots, and they say overfitting the actions in machine learning is bad, yet our neurons do become very strong and find the best actions/senses that we go by or avoid, plus can be ...
0
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0answers
21 views

Perceptron XOR calculation

I have following perceptron for the XOR-problem. I'm do not understand the perceptron or my calculations are wrong or both. My thought process: f.e. A is true and B is false. The function I use ...
1
vote
0answers
23 views

What are the most desirable properties of a neural network? [closed]

I'm trying to compare a custom neural network architecture with other existing ones. I'm quite new to the CS field and I'm looking for desirable properties and/or applications of neural ...
3
votes
1answer
43 views

What is the difference between 'features' and 'descriptors' in computer vision / machine learning?

I've read multiple time sentences similar to Finally, for standard image classification bag-of-words features based on SIFT descriptors have been found critical for high performances. We first ...
1
vote
2answers
31 views

What are the advantages of online learning when training neural networks?

Stochastic gradient descent with a batch size of 1 is apparently used to learn from single examples as they arrive, but I don't understand why you would use such a small batch size instead of batching ...
2
votes
0answers
55 views

What is the complexity of classification with SVMs?

I'm interested in how fast SVMs can classify new data with $c \in \mathbb{N}_{\geq 2}$ classes and $n \in \mathbb{N}_{\geq 1}$ features. Example for Neural Networks For neural networks, this depends ...
1
vote
1answer
60 views

Can an artificial neural network convert from cartesian coordinates to polar coordinates?

Given cartesian coordinates $x$ and $y$ as input, can a neural network output $r$ and $\theta$, the equivalent polar coordinates? This would seem to require an approximation of the pythagorean ...
0
votes
1answer
58 views

Dynamic Learning (Machine Learning)

I have been given a task to dynamically learn the optimum value of a parameter in a Heuristics Filtering Algorithm used in a tool. The accuracy of the tool increase as the value of K in the ...
0
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1answer
20 views

Backpropagation on a matrix of functions

As nicely described in this article, backpropagation for multi-layer perceptrons defines the error term for a neuron in terms of the partial derivative of the weights. It's traditional to represent ...
0
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0answers
19 views

What is a homogenous half-space?

I can't think of any definition for half-space that would involve some sort of quantity not being homogenous. This term is used in the paper Robust Concepts and Random Projection in the following ...
1
vote
2answers
48 views

Large number of layers in a neural network?

How large can the number of layers in a neural network be? Can there be maybe 1000 layers with, say, 1000 neurons in each layer? I imagine the brain has hundreds of thousands of layers.
9
votes
0answers
173 views

Pseudo-random sequence prediction

Disclaimer: I am a biologist, so sorry for (perhaps) basic question phrased in such crude terms. I am not sure if I should ask this question here or on DS/SC, but CS is the largest of three, ...
0
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0answers
35 views

How do I prove that the Perceptron bound for mistakes is tight?

How do I prove that the Perceptron bound for mistakes is tight? I need to prove that for any amount of given data points, the total amount of updates (mistakes) that the algorithm will make is ...
0
votes
1answer
20 views

How to identify labels in unsupervised learning?

Let's say I am working on handwritten digit recognition (0 to 9). I know for instance that if I use clustering then I need to look for 10 clusters. But once I have the 10 clusters,how do I identify ...
1
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0answers
34 views

Why naive Bayes performs better?

I have found that naive Bayesian classifier performs much better than classification using mixture of multivariate Gaussians. Here is the problem: I have a set of objects with attached features (10 ...
1
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0answers
30 views

Linear regression - iterative approach

I have a single output variable $y$ and a number of inputs $x_1$, $x_2$, etc. These are time series. Each $x_i$ explains the changes in $y$ in specific circumstances, and the goal is to have a linear ...
1
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0answers
2 views

Literature on nonlinear dictionary learning

Can anyone direct me to some literature on learning nonlinear dictionaries? I don't mean sparse dictionaries specifically, but for example learning kernel parameters, or learning $W$ where $y = ...
0
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0answers
12 views

Winnow versus Perceptron - Why adding irrelevant features increases L2(X) but not L∞(X)?

I saw here: http://www.cs.cmu.edu/~ninamf/ML11/lect0906.pdf Intuitively, if “n” is large but most features are irrelevant (i.e. target is sparse but examples are dense), then Winnow is better because ...
2
votes
0answers
32 views

What are the theoretical and practical contributions of Multiagent Systems to science?

Speaking about multiagent systems (MAS) is about as fuzzy as talking about artificial intelligence systems (AI). They are in essence the distributed counterpart of AI. While there are no so-called ...
2
votes
1answer
49 views

Difference between Bayesian Networks and Dynamic Bayesian Networks

I'm studying Bayesian networks and want to clarify a couple of things with people who are more knowledgable in the area than me. As far as I understand it, a Bayesian network (BN) is a directed ...
0
votes
2answers
68 views

Transforming training data for machine learning algorithms

If you want to make good predictions with machine learning (supervised learning in particular), you need a good training set. And relevant predictors in your feature set can be overshadowed by ...
1
vote
1answer
31 views

How can I compare two different neural networks, from a theorical point of view?

Let's say I have a problem (i.e. Given f(x), find x) and two neural networks(i.e. feedforward and recurrent). I would like to know if one works better than the other one. I could run the twos on a ...
0
votes
1answer
17 views

Effect of value of k in K-Nearest Neighbor

In K-Nearest Neighbor the value of k decides the accuracy of classification. What are the pros and cons of choosing smaller value for k and larger value for k?
1
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1answer
77 views

How do neural networks learn concepts?

I've been learning neural networks and some back propagation stuff, and I heard about google's Tensorflow and how it could learn things like how to carry on conversations. It got me thinking about how ...
2
votes
0answers
31 views

Removing common background in image processing phase for classification

I'm trying to build a machine learning classifier that can detect which object is in an image. I have a labled training set of images and am currently in the image processing phase. Each of these ...
0
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1answer
19 views

Confused by extremely high autocorrelation

I have the following python code ...
0
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1answer
18 views

Restricted Boltzmann machine confusing details

Hi I'm new to RBM and machine learning/computer vision in general, and I'm using the code from: http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/ while reading on ...
1
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0answers
28 views

How to train a Conditional Random Field/Markov Random Field Model for Object Classification in images given the features?

I want to classify superpixels in a image using a Markov Random Field/ Conditional Random Field model but have found no place that can help me with this (references don't even show how the network is ...
3
votes
1answer
36 views

Use subset of training data as prediction data

At our company, we've started using Amazon Machine Learning to predict the likelihood of a certain segment of our customers cancelling their subscription. We only have 500 customers in that segment ...
2
votes
1answer
29 views

Naive Bayes where Feature Space is LDA Output

I understand that the output for Latent Dirichlet Allocation is a distribution over K topics. Suppose I have a Dx(K+1) matrix, where rows are documents and columns are the topic distribution + one ...
0
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0answers
12 views

Implementation of Fourier Online Gradient Descend

According to the algorithm, is it true that I have to sample 1,...,D u's from the normal distribution with mean 0 and variance 1 (not sure what sigma square here refers to)? And then I have to ...
6
votes
3answers
77 views

Exploring and interpolating a function using machine-learning?

Which general machine-learning methods are there that try to "learn" or interpolate a smooth multivariate function and which get to actually choose the points at which the function is evaluated during ...
0
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0answers
14 views

what is the best theory/model to use for prediction in multivariate data?

I use a software for pollutant propagation on rivers that takes as input a set of parameters (p1, p2, ...pn) and creates an output file which is basically a matrix where on each row there is ...
4
votes
3answers
98 views

How are Neural Networks made so general?

After reading this blog about Deep Neural Networks learning about selfies I'm struck by how generic the network in question is. In short: I'm thinking of trying to write something vaguely similar for ...
1
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0answers
28 views

Language grammar correction with supervised learning

I want to work on automatic grammar correction using machine learning (possibly using recurrent or deep neural networks). The algorithm will be supplied with both corrected and initial documents for ...
1
vote
0answers
38 views

Teaching perceptrons colors? [closed]

I am learning about artificial neural networks and I've decided to go with perceptrons. I already made a sample program that can learn based on the learning data, but when I tried to make it recognize ...
0
votes
1answer
14 views

benchmark data set for Metareasoning problem

I am developing algorithm for solving following problem: Given a set of items with unknown feature(s) (but know distribution of feature(s)). Algorithm must choose what items to measure(every measure ...
1
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0answers
25 views

difference between predictive modeling and clustering?

What are the fundamental differences between predictive modeling and clustering? according to literature, predictive modeling is a supervised learning with aim to construct models to predict the value ...