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

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How to determine the optimal threshold for a classifier and generate ROC curve?

Let say we have a SVM classifier, how do we generate ROC curve? (Like theoretically) (because we are generate TPR and FPR with each of the threshold). And how do we determine the optimal threshold for ...
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2answers
45 views

Strategies for parallelising neural networks

When it comes to parallelising a problem, it involves the division of routines and subroutines between a number of nodes, namely; the master node and the slave nodes. Once each of these nodes ...
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1answer
53 views

Predicting continuous output

I'm trying to predict output per worker for given inputs of capital (physical capital), labor (human capital) & productivity. I have a data set of several countries ...
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2answers
50 views

What are the differences between AUC and F1-score?

F1-score is the harmonic mean of precision and recall. The y-axis of recall is true positive rate (which is also recall). So, sometime classifiers can have low recall but very high AUC, what that ...
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66 views

Rademacher complexity of logistic regression

Consider logistic regression. We have the logistic loss function, $\phi: R\rightarrow [0,1], \phi(u)=\log(1+\exp(-u))$, which is Lipschitz, and we have the linear function class $F=\{f_w:R^d ...
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1answer
61 views

How to know which feature mainly led to the prediction?

I have a classification problem where I use a model (say Logistic regression or SVM) to determine whether an instance belongs to class 0 or class 1. For a certain prediction on a test instance X, if ...
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19 views

Create a damping function for discrete time series data such that values converge to constant value

I have an agent-based model where an agent predicts output and then compares that value to the actual output. How can I create a damping function of sorts that will cause the delta between expected ...
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27 views

My support vectors don't look correct

I am trying to classify a toy dataset using SVM. I only have two features and 20 instances. The decision boundary seems correct, however, the support vectors dont look correct. This is the relevant ...
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41 views

Unsupervised learning

I am implementing a paper titled as "Internet Traffic Identification using Machine Learning" by J. Erman et al.. I have completed supervised part, but now I got stuck in unsupervised learning part. ...
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19 views

Positive and negative examples in Rocchio-based recommender

I am exploring the usage of Rocchio-based recommenders in e-commerce and news portals and trying to wrap my head about the concept of a negative rating. Often in e-commerce or news portals there is no ...
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22 views

Why we even try to minimize a loss function, which is non-convex, in matrix factorization?

In matrix factorization (especially under the scenario of recommendation system), we often try to factorize a matrix Y into two low rank matrices: $Y=U\cdot V^T$ If we assume there $m$ instances in ...
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26 views

How to increase Accuracy in Age model on Mobile operator dataset (Call log)?

I know question is too lengthy, but it need description. I am currently working on predictive analysis project. Which predict customer age, gender, etc. demographics attributes from their data set. ...
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14 views

Active Learning for Tweets Classification

I am working on a binary classification problem on Twitter, in which I have to classify tweets in one of two categories. Now I would like to test how different active learning frameworks would work ...
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1answer
39 views

Is it possible to combine several clustering results in a meaningful way?

The problem I face is somewhat awkward, I have 40,000 points in my dataset and I would like to cluster them hierarchically. But due to the limitation of my laptop(and R) in each run of clustering only ...
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0answers
21 views

How do I represent semi-supervised learning in plate notation?

In a plate notation, how do I represent variables in which both labeled and unlabeled instances exist? For example, let's say I have an Latent Dirichlet allocation-like algorithm in which some ...
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2answers
63 views

Help with understanding statistical measures and Receiver Operating Characteristics

In my machine learning class we just went over statistical measures and plots. We looked at the definitions of True Positive Rate (sensitivity/recall, etc), 1-False Positive Rate (speciicity), ...
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3answers
950 views

Why downsample?

Suppose I want to learn a classifier that predicts if an email is spam. And suppose only 1% of emails are spam. The easiest thing to do would be to learn the trivial classifier that says none of the ...
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36 views

Understanding kernel PCA

Kernel SVMs are explained as follows: Apply kernel method to original data Check if we have a linear separator in the kernelized space. Map linear separator back to original space Is it fair to ...
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1answer
46 views

How to solve this problem on Curse of Dimensionality problem - Nearest Neighbours

I have started learning classification techniques and trying to solve the problems from the book Introduction to Statistical Learning. While currently working on the which is based on Curse of ...
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17 views

How do I implement custom sparse connections in a neural network?

We'd like to implement neural network which is not fully connected - we want to explicitly set which output neurons connect to which input neurons (there's no hidden layer). We use Theano but we can ...
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11 views

Applying linear function approximation to reinforcement learning

How do you apply a linear function approximation algorithm to a reinforcement learning problem that needs to recommend an action A in a specific state S? I've read over a few sources, including this ...
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35 views

How to compare two ranking algorithms?

I want to compare two ranking algorithms. In these algorithms, client specifies some conditions in his/her search. According to the client`s requirements, these algorithm should assign a score for ...
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2answers
68 views

How to do cross-validation when comparing different feature selection methods?

I am using SVM for a prediction task. My sample size is small, only N=140. Suppose I want to compare the prediction accuracy when using two different feature selection methods. Would it be better to: ...
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1answer
57 views

When normalization is counter-productive [duplicate]

Could you give me general examples of when normalization is not used properly and affects badly the classification accuracy, or when it is not needed?
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10 views

Event level driven response modeling

I am investigating operational and maintenance data for a fielded system. There is a year worth of data. The operational data has been reduced to fault indications, which are triggered when ...
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8 views

Y axes on the logit scale and centered in gbm.plot [migrated]

I am currently exploring the gbm functions in the package dismo to create boosted regression trees for species distribution modeling. I have been using the dismo vignettes as well as the 2008 paper "A ...
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2answers
69 views

Does Support Vector Machine handle imbalanced Dataset?

Does SVM handles imbalanced dataset? Is that any parameters (like C, or misclassification cost) handling the imbalanced dataset?
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32 views

difference between machine learning and stastitical technique [duplicate]

Is there any difference between machine learning and stastitical techniques. I have searched a lot some researchers say that there are some overlap some are saying there is no difference.Can you give ...
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16 views

Assessing predictor contribution to model output

Many of machine learning methods are considered as "black boxes". Examples of such methods are SVM, Neural Networks, Random forests etc. One may apply sensitivity analysis techniques (as described for ...
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1answer
32 views

Stratified sampling for creating test/training sets when there are continous and categorical variables to consider?

Assume a simple clinical study with N=200. Half of the participants are men and half of the participants are women. The hemoglobin of the participants ranges between 80 and 150. There's also several ...
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44 views

What are the advantages of deep convolutional neural network over shallow one?

I know that deep convolutional neural network(cnn) helps reducing the number of free parameters in training. What are other advantages of using deep cnn over shallow cnn?
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2answers
44 views

Can we learn 3d features using Autoencoder?

Typically, we use Autoencoder to learn 2d features on 2d images (e.g. pen-strokes of digit). For example, if I have 10000 3d 31x31x31 images (e.g. car images). I unroll each of the images, i.e. ...
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19 views

Naive Bayes Produce Confidence

I am pretty newbie in machine learning. Please forgive and point out anyone incorrect use of terminology. Now I am learning Naive Bayes algorithm. As I have learned Neural Network, when predicting, ...
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3answers
233 views

When should I apply feature scaling for my data

I started a discussion with a collague of mine and we started to wonder, when should one apply feature normalization / scaling to the data? Lets say that we have a set of features with some of the ...
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1answer
42 views

Why AUC-PR increases when the number of positives increase?

I asked a question earlier about comparing models using Precision-Recall AUC. One of the answers included the following statement: "The larger the fraction of positives in the data set, the larger the ...
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1answer
72 views

What are some useful robust and scalable approaches towards anomaly detection of a time series data?

What are some useful robust and scalable approaches/methods towards anomaly detection of a time series data? I am mainly looking for some practical approaches carried out using Python, R, Java, etc. ...
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2answers
58 views

Comparison of machine learning algorithms

Suppose i have taken 8 machine learning algorithm which is used researchers more frequently.I have applied these 8 machine learning algorithm over 8 datasets which is publickly available on internet. ...
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0answers
17 views

How to approach a bag-of-words classification when each word has a 'loudness' parameter?

Suppose that I want to perform a binary classification on voice data, classifying sentences as having a positive/neutral or negative sentiment.The language I'm working with only has 50 words total and ...
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1answer
41 views

Ranking two models based on ROC-AUC and PR-AUC

I have two methods/classifiers (completely different models) that I need to decide which one is better. The dataset is imbalanced. I trained both classifiers on the same dataset and then I computed ...
2
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1answer
35 views

Is it correct to use Precision-Recall AUC in a balanced dataset situation?

I have a binary classification scenario with a dataset that is unbalanced (much more negatives than positives). When I train a classifier on this dataset I get a Precision-Recall AUC of 0.7. Then I ...
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0answers
29 views

How to calculate the area under the precision-recall curve for the random classifier?

I know that the random classifier score in ROC AUC (Area under the curve) is always 0.5. My question is: how to calculate the Area under the precision-recall curve for the random classifier?
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22 views

why pretraining for convolutional neural networks

Usually Back propagation NN has the problem of vanishing gradients. I found that Convolutional NN (CNN) some how get rid of this vanishing gradient problems (why?). Also in some papers some ...
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42 views

How to plot a precision-recall curve when doing cross-validation?

I'm using cross-validation to evaluate the performance of a classifier with scikit-learn and I want to plot the Precision-Recall curve. I found an example on ...
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13 views

Optimizing selection from varying sets

On pages of website(s) I have a set of potential messages to choose from and only one or two slots to show them in. (think 'this product is on sale' or 'this product is new'). On each page the set ...
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32 views

What does “shift invariant” mean in convolutional neural network?

I saw a term describing the feature detectors, i.e. shift invariant. What is that mean? Paper: 1989 Generalization and Network Design Strategies
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22 views

Should a BoxCox transformation to normalize the skewness of data be applied to all the predictors?

If there are few predictors that are highly skewed among a larger set of predictors in case of a linear regression problem, should a BoxCox transformation be applied to only these few predictors or ...
0
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1answer
27 views

How do search engines generate related searches?

I would like to know how search engines like Bing generate related searches when the user starts typing into the search box. From what I gather, there has to be some sort of a ranking algorithm where ...
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1answer
19 views

Which Machine Learning algorithm: Sorted list of tags given metadata?

Our system allows an admin to manage a database of university courses. These courses have multiple fields, like the department, a title, and a description. I am adding the ability to add learning ...
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30 views

clustering analysis of large amount of time series

I would like to cluster a set of time series, which are composed of around 50000 different time series. Are there established algorithms/package that can handle this scalability problem?
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7 views

Function approximation, inverting and finding input values subsets based on output value

Suppose I have a set of input/output values for some unknown and complex function which I want to approximate using some machine learning algorithm. The input variables are integers or reals. The ...