Methods and principles of building "computer systems that automatically improve with experience."

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Response surface of a particular discontinuous function

I have a function IR that depends on several (maybe 100) input random variables. I know ...
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35 views

A new piece of clue for document classification?

I am working on a document classification problem. I am using the typical vector space model to represent a document as doc-term vector. If document has some term, the vector entry for that term is ...
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42 views

Conceptual issues in training neural network and learning curve

I have a 4 Input and 3 Output Neural network trained by particle swarm optimization (PSO) with Mean square error (MSE) as the fitness function using the IRIS Database provided by MATLAB. The fitness ...
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1answer
34 views

How to implement a hold-out validation in R

Let's say I'm using the Sonar data and I'd like to make a hold-out validation in R. I partitioned the data using the createFolds ...
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23 views

Fisher's Exact Test to assess the significance of a difference between false positive rates

I have trained two binary classification models on the same data and evaluated them using the same test set. For each model I have calculated a false positive rate (and a count of false positives) at ...
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13 views

sub-questions with likert scales choices

I want to determine compliance to a certain standard in ISMS and to determine that, I have the standards started and it has sub-questions (say 2,3,4 questions), with each having a 5 point likert type ...
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102 views

Random forest on multi-level/hierarchical-structured data

I am quite new to machine learning, CART-techniques and the like, and I hope my naivete isn't too obvious. How does Random Forest handle multi-level/hierarchical data structures (for example when ...
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15 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 ...
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46 views

Why can we use entropy to measure the quality of a language model?

I am reading the < Foundations of Statistical Natural Language Processing >. It has the following statement about the relationship between information entropy and language model: ...The ...
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36 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: ...
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28 views

Estimating training values in machine learning

I've started to learn machine learning using the book 'Machine Learning' by Tom Mitchell. The first chapter introduces the reader to machine learning and to the first problem: learning to play ...
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1answer
22 views

ML for specific classification problem

I have a training dataset for classification problem $X \rightarrow y$. Where $X$ is an $n$th dimensional real vector, $y$ is an integer number in $\{0, 1\}$. I want to solve the next problem: ...
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102 views

Not very known but effective data mining algorithms?

I recently came a cross Matrix Factorization for classification or recommendations. I was very shocked that it was one of the most effective techniques in the Netflix competition. But actually I think ...
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34 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 ...
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89 views

Would there be a model selection problem if we had access to an oracle that gave us the exact generalization error?

Let $\mathcal{E(h)}$ a function that given some hypothesis $h$ returns the generalization error for that fixed $h$. I was reading some notes about model selection and generalization error and it ...
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2answers
44 views

Ensemble of models with different feature spaces

BACKGROUND I have data in which the dependent variable is binary with a highly-skewed distribution: <1% records are 1 (doers), >99% records are 0 (non-doers). I'm using logistic regression to ...
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24 views

Handwriting Recognition - Percentage Match

I'm currently working on a senior project and we've chosen handwriting recognition. Initially I thought that using machine learning algorithms were a good idea for this, but after the thought below ...
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106 views

How to calculate p values in logistic regression with gradient descent algorithm

In logistic regression, the gradient descent algorithm for calculating coefficients can be described this way: Until convergence, do $$ \beta := \beta + \alpha \frac{\partial L}{\partial \beta} ...
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1answer
21 views

how to compute minimum required vc dimension for a classifer to classify a specific data

Suppose we're given an N dimensional data to classify. To cope with this task we may choose a classifier that suits our desires more. However obviously not every classifier is capable of classifying ...
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21 views

Evaluation indexes hypothesis for clustering

I read on the cluster analysis page of wikipedia: For example, k-means clustering can only find convex clusters, and many evaluation indexes assume convex clusters. On a data set with non-convex ...
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11 views

k-way MinMax spectral clustering

Is there a k-way MinMax Cut Spectral Clustering which can be easily implemented? In the spectral clustering tutorial I found only 2-way MinMax cut.
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12 views

Record linkage when sources have different fields

I have read a little about record linkage, but it seems to me that a requirement is that all fields in both sources can be compared. For example, with sources A and B, an assumption is that we can ...
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1answer
46 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 $K(K'(x))$ ...
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1answer
25 views

SVM decision boundary conditions : derivation problem

I was trying to understand the derivation of SVM decision boundary. Suppose my decision boundary is y-x-1=0. Now in the book it was written that ...
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Comparison of cophenetic correlation coefficients on different data sets

I applied the same hierarchical clustering (weighted) on two data sets: The first is a 'raw' data set, on which I didn't do anything, and the second on the same data set after I filtered it by ...
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52 views

How can I perform 10-fold cross validation by manually constructing datasets?

I am working in text classification in RapidMiner where, because of the nature of my problem, I cannot use the built-in k-fold cross validation strategy, so I decided to create 10 copies of my dataset ...
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processing a sound file for analysis of different spoken languages

So i have sound files for 5 languages by 2 person, thus my input data has 10 sound files. Now i want to cluster them based on the languages (thus 5 clusters) and not based on the speaker/voice ( ...
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33 views

Linearly dependent features

I have a matrix A of 1000 observations (rows) and 100 features (cols). I would like to find: Linearly dependent features so that I can remove them and simplify the problem. rank(A) gives me 88, ...
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38 views

HIstogram of oriented gradients (HOG) features descriptor theoretical problems

I'm going to implement HOG as my features descriptor. But there are some things that make me confused: For example: If we have an image with size of 10 x 20 If we want to compute the HOG of that ...
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33 views

support vector machines software implenentation

I don't have a background of computer science but i have my major in mathematics , i have interests in support vector machine i went through theory as well as some practical examples the problem with ...
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23 views

Literature for prediction models where each training example has a different amount of data?

This could be a machine learning question as much as a statistics question, but I think this is the best place to put the question. Here are three different examples of problems where each ...
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24 views

Converting a spearman correlation to a euclidian dissimilarity

I am applying ward hierarchical clustering on a data set for which I have pairwise similarities. Since hierarchical clustering need a dissimilarity matrix, I am trying to convert my similarity matrix ...
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14 views

Persistent Contrastive Divergence for RBMs

When using the persistent CD learning algorithm for Restricted Bolzmann Machines, we start our Gibbs sampling chain in the first iteration at a data point, but contrary to normal CD, in following ...
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1answer
21 views

How to Build a Foresight System?

For a research project, I'm asked to find ways to build an economic foresight system. For example, for the production of cheese. We will have data about the market indicators, like price, demand etc. ...
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1answer
82 views

Relative importance of a set of predictors in a random forests classification in R

I'd like to determine the relative importance of sets of variables toward a randomForest classification model in R. The ...
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78 views

How to calculate disparity of two images in matlab?

I have two images and I am trying to calculate the disparity between them using sum of squared distances and reconstruct disparity in 3D space. Do I need to rectify the image before calculating ...
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How Does a Disparity in Number of Documents (Training Data Points) Affect Text Classification?

I have collected a fairly clean set of data (5,410 documents) to train a text classifier. I am now attempting to improve my classification success. (Note: When I trained/tested the classifier from ...
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61 views

My data lies on a linear plane

My purpose is to classify 3 classes from an EEG data. When I plotted my data on feature space in order to visualize, I found they lie on such a linear plane (please see my figures). Before plotting, I ...
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26 views

clustering in Item-based collaborative filtering

I read about item-based collaborative filtering in the paper from Sawar et al. I want to apply clustering on items to find the most similar items and then apply the prediction. Is this a good ...
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27 views

What would you use as your default model for comparison?

I have a question. Say you are given with like 1500 instances as your dataset, in which instance will be categorized into 1 of the 3 classes. You're supposed to come up with the best model from the ...
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Is support vectors algorithms dependent?

In SVC problem, given all the coefficients fixed (C, gamma, etc), is it possible to get different decision functions and support vectors with different optimization strategies?
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Training models for classification using different negative datasets

I'm working on a massively unbalanced binary classification task - Classification of given protein sequences as belonging to a certain (very small) class, or not. There are about 1,300 positive ...
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“Robust” normalization of features from multiple groups and unknown distributions prior to learning

I'm working on a machine learning project involving statistical analysis (and later discriminatory classification) of different proteins (samples) drawn from multiple, potentially overlapping classes ...
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22 views

What is the difference between a neural network and Deep neural networks? [duplicate]

Please i m looking for tutorial about neural networks and deep neural networks, -Architecture -Training -etc... Thank you :)
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38 views

ML algorithm to find optimal control parameter

I have a training dataset $(X, y) \rightarrow z$. Where $X$ is an $n$th dimensional real vector, $y$ is an integer number in $\{1, 2, 3\}$, and $z$ is a real number. I am looking for machine learning ...
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27 views

Support Vector Machines - Kernel Functions/Soft Margin SVM

I had these questions in an exam today. State True or False and explain. If k1(.,.) and k2(.,.) are two valid kernel functions, then if h = k1 - k2, is h(.,.) a valid kernel function? A standard ...
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41 views

Sample space for hypothseis, training data of bayes theorem

I am learning about Bayes theorem in machine learning . $p(h/D) = \frac{p(D/h)p(h)}{p(D)}$ $p(h) = $prior probability of hypothesis h $p(D)$ = prior probability of training data D $p(h/D)$ = ...
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modeling rates with machine learning tools (svm, gbm, nnet)

I have a numeric integer variable that is knowly proportional to an exposure measure plus other continuous / categorical covariates. If I were to use classical log-linear glms i would model ...
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20 views

Difficulties in applying activation function in neural network

This is beginner level question. I have several training inputs in binary and for the neural network I am using a sigmoid thresholding function ...