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

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3
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+50

Unsupervised Learning: BCM or Oja's Rule

I am learning about unsupervised machine learning, and am a bit confused regarding different algorithms to update weights. So, I understand that both Oja's Rule and BCM can be used. In Oja's rule: ...
0
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0answers
6 views

UCI Machine Learning Data Set: Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set [migrated]

I would like to use the data set Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set from UCI to test pattern recognition algorithms. However when I plot the features and ...
2
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2answers
36 views

Huffman tree generation if the frequency is same for all words

Can a valid Huffman tree be generated if the frequency of words is same for all of them? Example : ...
2
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0answers
7 views

What is the meaning of the output weights of a Conditional Random Field (CRF) model?

Problem When train my linear chain CRF with annotated observations, I feed it with a number of sequences containing observation values and a "ground-truth" label for each observation. I'm currently ...
0
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0answers
33 views

Standard problem sets for metaheuristics

I'm wanting to dabble with metaheuristics and am interested to know what the "hello world" problem sets are. In other words, what are the common problems (e.g Traveling Salesman, Vehicle Routing ...
0
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1answer
33 views

How to use frame based speech features for learning using a neural network classifier?

I am doing supervised learning on speech audio files using neural networks. For this purpose, I'll have to extract features from the audio file. But since an audio file is a time varying signal, it is ...
0
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1answer
28 views

A clarification on the taxonomy of Evolutionary Algorithms

A rather basic question but I am confused about the characterization of a certain local search method which I want to describe in the framework of EAs. In particular, consider an EA which in every ...
2
votes
1answer
35 views

Choice of machine learning algorithms frequency of parts of speech

I'm new to machine learning. I have text, and I tag the text according to their parts of speech tag ie walk is tagged as verb, etc. I tag entire sentences, and then convert them into a vector based on ...
7
votes
1answer
30 views

PAC learning model definition

The probably approximately correct (PAC) learning model is defined as: A concept class $C$ is said to be PAC-learnable if there exists an algorithm $A$ and a polynomial function $poly(·,·,·,·)$ such ...
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30 views

An example for a finite hypothesis class which is not PAC learnable?

I know that with a bounded loss function, every finite hypothesis class is PAC learnable. Are there examples for non PAC learnable hypothesis classes with an unbounded loss function?
0
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0answers
18 views

Optimal Design of Cascaded Classifier

Consider a cascade of classifiers and a binary classification task. Cascade consists of some number of strong classifiers (n) each of which consists of some number of weak classifiers (m_i, where i = ...
3
votes
1answer
34 views

MAP estimation (for stationary iid gaussian environment)

This is my first post, and have been self studying Haykin's Neural Networks and Learning Machines book. I'm not sure if this is a typo or if I'm doing something wrong, but I've been stuck on a ...
0
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0answers
8 views

Finding the minimal set of n-tuple instances for a training set, to be used in Candidate-Elimination algorithm

The Candidate-Elimination algorithm(which may go by another name), is an algorithm which received a set of instances for input and outputs a General and Specific hypothesis. The algorithm can be seen ...
0
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1answer
29 views

Online supervised learning algorithm

I have labeled examples coming in on the fly, thus I need to create a classifier from sequential data instead of a static example set. Incoming data is fully labeled, there are no unlabeled examples. ...
4
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0answers
153 views

some kernel and greater margin, how this occures?

I read following notes, and couldn't get it. any idea or hint would highly appreciated. a SVM classifier using a second order polynomial kernel. The first polynomial kernel maps each input data x to ...
-1
votes
2answers
58 views

Different weight sets for same problem?

Considering the case that we have a fixed set of training examples and a fixed ANN (i.e same number of input,output and intermediate layers). Is it possible that there exists more than one set of ...
0
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1answer
19 views

Kernel Perceptron vs Polynomial Perceptron

I was looking at Support Vector machines (SVM) kernels. Looking at Polynomial Kernel and Kernel Perceptron I was curious how they differ? Work Done Polynomial Kernel: ...
0
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0answers
14 views

Any good algorithms for 3d volume alignment by feature extraction, especially ones involve machine learning?

I am trying to align 3d mri brain images of different rat individuals. I have dozens of examples that have been aligned manually, which are good resources for machine learning. I am considering ...
0
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1answer
38 views

Transformation from one feature space to another

I have found the following example: As an example consider the case when the input space $ {\mathcal{X}}$ consists of images of $ 16\times 16$ pixels, i.e. $ 256$ dimensional vectors, and we ...
0
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0answers
20 views

Learning a regex from string examples? [duplicate]

Given some strings, is there some algorithm (and program that implement such an algorithm) that can create a regex which matches some of the given strings and not the other given strings? Note that ...
-1
votes
1answer
26 views

Collecting data(images) using a crawler [closed]

Please let me know, if this goes here, if not, please point out where I should post this. Thanks in advance. So I require huge number of training data, mostly images. This is a pet project, only for ...
4
votes
1answer
52 views

PCA and Eigenvectors

I am trying to understand how PCA works, and think I got most of it except... By calculating eigenvalues/vectors of the covariance matrix of the original dataset allows to find those dimensions where ...
1
vote
1answer
42 views

Classification when some classes are dependent

I think my problem can easier be explained via an example: Assume we have a dataset containing the images of 10 different mammals, let's say lion, elephant, cat, ... and horse. We have a 20-class ...
1
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0answers
23 views

Research work on computational models for a “specific” person's behaviors

Is there active research work on creating computational models of a "specific" person's behaviors (general behaviors, emotions, actions...)? What are some references for such research? I tried google ...
0
votes
1answer
26 views

Text features in decision tree

Right now I am doing some problems on application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, ...
1
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0answers
37 views

Maximum likelihood estimate for softmax function

Given an undirected graphical model with no edges and only N nodes, I am trying to find a closed form solution to the ML estimate of each node given that $p(x|\theta)=\frac{\exp(\sum_{s\in ...
0
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0answers
20 views

DAG that can capture any joint distribution

I am trying to do the following question: draw a directed acylic graphical model on five variables which can capture any joint distribution. I'm not sure I understand what it means by "can capture any ...
6
votes
2answers
80 views

PAC learning axis parallel rectangles

I am trying to understand the proof that the axis parallel rectangles are PAC learnable in the realizable case. This means that given $\epsilon, \delta$ with enough data we can find a function $h$ ...
3
votes
1answer
38 views

What does does $O$ mean in this context?

I understand big O notation in computational complexity theory, but I don't see how it applies in the equation below. From Pattern Recognition and Machine Learning: If we weren't familiar with ...
1
vote
1answer
66 views

Bayes net: algorithm to calculate joint distribution?

I recently started studying bayesian networks and I am now implementing an exact inference algorithm: enumeration. I am aware of the complexity and inefficiency of this method but I want to fully ...
2
votes
1answer
38 views

What is an edge hop?

I've tried googling it, but found nothing. Here is the context it's in: From Bayesian Reasoning and Machine Learning: Adjacency matrices may seem wasteful since many of the entries are zero. ...
2
votes
2answers
60 views

What happens when you don't use a metric in k-means?

K-means is a clustering algorithm which works like this: ...
1
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0answers
39 views

Hessian-Free instead of LSTM for Recurrent Net Machine Translation

Last year, Ilya Sutskever and collaborators came out with a paper about a recurrent LSTM net that learns sequence to sequence mappings for machine translation. It's somewhat surprising that the ...
0
votes
1answer
53 views

Build Automatic Speech Recognition (ASR) from scratch [closed]

I want to build a Automatic Speech Recognition (ASR) engine for myself, but I've no idea from where to start. I've read that most ASR's are build upon Hidden Markov Models, but also I've read that ...
0
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0answers
18 views

Amplifying a Locality Sensitive Hash

I'm trying to build a cosine locality sensitive hash so I can find candidate similar pairs of items without having to compare every possible pair. I have it basically working, but most of the pairs in ...
0
votes
1answer
41 views

Why is the most probable assignment for all variables in MRFs called MAP assignment?

I am new to graphical model, especially Markov Random Fields. I have a question about MAP assignment. Let say we have the graph structure and all the potential functions. MAP assignment in MRFs is ...
8
votes
0answers
108 views

Who coined the term “machine learning”?

I'm trying to figure out who coined the term "machine learning". An ancillary question is from where is Arthur Samuel cited as defining the field of "machine learning" in 1959 as: the field of ...
0
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0answers
35 views

where should I begin : I need to predict power consumption of a household?

I have the power consumption values of a household for 2 year. To preserve privacy I am down sampling this data before sending it to the utility company. My goal is to get back the original data from ...
0
votes
1answer
26 views

Adding concept drift to data sets

I'm about to work with concept drift problem in data streams. I need to start with real data sets from UCI machine learning repository and add to them concept drift (in attributes domain). Do you ...
2
votes
2answers
45 views

Learning Quadratic Functions

I have seen in some ML tutorial that functions of the form $f(\vec x) = \vec x^T A \vec x$ ($\vec x \in \mathbb{R}^n$ and $A$ is an $n\times n$ real matrix) can be PAC learned. Can anyone point me ...
2
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0answers
25 views

Ridge regression with more small errors

I've been using kernel ridge regression. My problem requires predicting some values which I know to be integers. By rounding the results to the nearest decimal, I get excellent results. However, If ...
1
vote
1answer
178 views

Cognitive Computing vs Artificial Intelligence?

Can anyone please tell me the difference between them? A brief definition of Cognitive Computing would appreciated. Also how does cognitive computing relate to neural networks? Thank you~
2
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0answers
41 views

How regression is used in item-based collaborative filtering?

In the paper "Item-Based Collaborative Filtering Recommendation Algorithms" In section 3.2.2 about regression, it is said the the user's actual rating of item N (Ru,n), is replaced with an estimate ...
0
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1answer
44 views

How does the ID3 Algorithm differ from a generic Decision Tree learning algorithm

Based on the notes of my Machine Learning lecturer, I am struggling to understand how the two algorithms differ? Both seem to select the most informative feature A (based on least entropy), then ...
0
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0answers
43 views

Is a KNN-Classifier memory intensive?

In my opinion it seems fairly obvious that a $k$ nearest neighbours (KNN) Classifier would be fairly expensive in terms of memory, as the model is the training set itself. However, any notes I've read ...
0
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0answers
23 views

Learning Hierachial representation of objects in a unsupervised manner

I am trying to understand how the how a hierarchal representation of an object can be learned in a unsupervised manner, using the method described in this paper. ...
4
votes
1answer
199 views

Genetic Algorithm, Neural Network, Deep Learning, Machine Learning Similarities and Applications? [closed]

I am a computer engineering student and trying to get the idea behind all these Artificial Intelligence Concepts and applications. I know little theoretically about machine learning and some high ...
2
votes
2answers
187 views

ANN - Backpropagation with multiple output neurons

Can I utilize the backpropagation algorithm in a layered, feed-forward ANN in instances where there are multiple output neurons? If so, how? Links to (somewhat) comprehensible resources would be ...
0
votes
1answer
36 views

what is the general name of this problem?

what is the general name of a problems where learning agent observe new data as a learning goes on. For example when playing platformer games one must incrementally learn new level areas and states ...
1
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0answers
42 views

Formula for number of parameters in an undirected graphical (probability) model

I have googled endlessly, and I cannot find it. Can anyone point me to a reference that gives a way to calculate the number of parameters in an undirected Graphical Model? Adapting from the similar ...