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

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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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1answer
49 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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18 views

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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1answer
32 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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32 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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1answer
32 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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23 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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13 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
76 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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2answers
60 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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0answers
12 views

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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59 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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25 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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15 views

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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11 views

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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20 views

“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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36 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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25 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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1answer
40 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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16 views

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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16 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 ...
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0answers
33 views

Understanding and Implementing a Dirichlet Process model

I am trying to implement and learn a Dirichlet Process to cluster my data (or as machine learning people speak, estimate the density). I read a lot of paper in the topic and sort of got the idea. ...
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17 views

What is data augmented by the additive inverse?

I am reading Biclustering of expression data (Cheng and Church, 2000) The paper is about the Cheng and Church biclustering algorithm and its main metric, the mean squared residue (MSR). It is said ...
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11 views

What sort of feedback button should I implement to measure the difficulty of a task

---background info--- My company has built a Search Engine Optimisation product that can suggest monthly prioritised SEO activities (task suggestions), that will have the most benefit to the end user. ...
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29 views

Converting a similarity to a dissimilarity [duplicate]

I am working on a clustering problem for which I have to manually choose the number of clusters. I have a visualization tool that helps me decide whether the clusters are good. In order to ...
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1answer
25 views

Can a deep belief network (stacked RBMS) be used solely as a dataset generator?

I have a large dataset (tens of thousands of predictors) on which I would like to perform feature reduction with the intent of better model-building for prediction. Deep Belief Networks seem to ...
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22 views

What is a shift bicluster?

I am reading A comparative analysis of biclustering algorithms for gene expression data (Eren, Kemal, et al. - 2013) When explaining the Cheng and Church method, it says that: MSR was shown to be ...
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2answers
43 views

Splitting an imbalanced dataset for training and testing

I have a highly imbalanced dataset. My question is how to split the dataset for training and testing? I want to have a separate training set and a separate testing set. One idea I had is just to ...
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3answers
96 views

The value of adding the ROC graph if the AUC is given

I always see in papers that when they want to show how they classifiers performed, they provide ROC graph and the AUC score. Now as far as I know only the AUC tells how well the classifier performed, ...
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0answers
41 views

Time series prediction where each datapoint has a sequence

I am Computer Science major, and new to stats, so please bear with me and point me to the right direction if what I'm asking is pretty obvious. I have a dataset, where each data point consists of ...
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1answer
197 views

What is the right attitude toward open source machine learning toolkits?

There are lots of machine learning toolkits nowadays, such as weka, sklearn, R libs. If we choose to use these toolkits, besides that it is convenient, sometimes ...
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1answer
49 views

How to perform parameters tuning for machine learning?

I have a very basic question regarding parameter tuning using grid search. Typically some machine learning methods have parameters that need to be tuned using grid search. For example, in the ...
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47 views

In EM derivation why can I sum over the iid variables in the conditional expectation?

In EM when you take the expectation: $E[\log P(y,x \mid \theta)\mid x, \theta']$ $= \sum\limits_yP(y\mid x, \theta') \log P(y,x\mid \theta)$ I understand this but the following part I don't ...
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1answer
48 views

Imbalanced training dataset and Random Forest regression model

I have a large dataset (>300,000 observations) that represent the distance (RMSD) between proteins. I'm building a regression model (Random Forest) that is supposed to predict the distance between any ...
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15 views

Difference between summing and multiplying covariance matrices?

Say we have an RBF covariance matrix A and some periodic covariance matrix B for a given dataset. Covariance matrix A says that you believe that points that are close together are somewhat similar, ...
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15 views

Which approaches are known to extract product / service data from previously unknown webpages via machine-learning?

To avoid reinventing the wheel, which approaches are known to extract product / service data from previously unknown webpages via machine-learning? Which keywords in a search engine might give me ...
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34 views

Precision and recall Break-Even performance

I am using WEKA to do classification. I wish to compare with an existing paper that uses the Precision and Recall Break-Even performance as their results. Can someone please help in order for me to ...
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36 views

anomaly detection on logs data

I wonder , if someone know the open source anomaly detection algorithm on computer log ? For an example , computer log look like as mentioned below : "value UL-CCCH-Message ::= { integrityCheckInfo { ...
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1answer
13 views

feature representation for DNA bases classification

I'm currently dealing with large DNA sequences for machine learning purposes, I'm basically improving existing methods. What I have is several millions of DNA sequences : ACGTAGGCAGGCTTTC ... In ...
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28 views

Vector Quantization of heavy tailed distribution

I'm generating with Monte Carlo simulation some stock price $X$. Once I have the stock price sample, I want to cluster it with 100 points $\hat{X}$. My problem is that the error associate with my ...
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2answers
58 views

Do discriminative models overfit more than generative models?

In an interview, the interviewer said that discriminative models tend to overfit more than generative models because they solve a more complex problem and hence consume more resources (or parameters) ...
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22 views

What to do for Dirichlet distribution when elements of X vector may be zero

So I want to define a Dirichlet distribution over frequency vectors which are unit vectors whose elements represent the frequency with which different characters occur in a body of text. Trouble is ...
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1answer
24 views

Data Conversion to Standard data format in hierarchical Dirichlet process

I'm trying to test the performance of posterior inference on a set of documents with hierarchical Dirichlet process for topic modeling. How can i convert my data (document) to standard data format ...
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1answer
22 views

Offline training or batch wise training

Can somebody please explain how to train a neural network in batch mode. I have a single target time series of length $N$ for a given input time series of the same length. In order to apply Hopfield ...
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1answer
30 views

Grid Search for hyperparameter and feature selection

So I need to select my hyperparameters and also my features. A full grid search of the space of hyperparameters and features is too computationally intensive, so what I am doing instead is for each ...