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

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Should the 'TPR of adaboost' be better than base classifiers'?

There are two different base classifiers which produce true positive rate (TPR) values 99.46% and 91.79%. When I use these base classifiers in adaboost, what should the new TPR be? Better than two of ...
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1answer
31 views

Multi-armed bandit in face of full reward information

I am new to this area of machine learning. I am just walking myself through UCB1 algorithm which seems to assume that the payoff can be learnt only for action that ...
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1answer
49 views

How does cross-validation and the Bayesian method overcome the overfitting problem?

I was told that cross-validation and the Bayesian method can overcome the overfitting problem. I was told that comparison of models in a Bayesian way is actually doing cross-validation... What is the ...
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17 views

Feature learning with a deep learning aproach?

How to create a feature vector from text with a deep learning aproach?. Im new at this topic, could anybody advice me where to start and how to aproach this task?.
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2answers
143 views

Bayesian lasso vs ordinary lasso

Different implementation software are available for lasso. I know a lot discussed about bayesian approach vs frequentist approach in different forums. My question is very specific to lasso - What are ...
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1answer
29 views

How to : a brief intro to scaling and rescaling data ( inputs) for supervised learning algorithms

I understand the concept of scaling and that it improves results in SVM's and NN's. however I would like to find somewhere where is is explained, in easy "layman's terms" terms. of how it is done. I ...
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37 views

What are appropriate validation methods for a Bayesian network model with low sample size?

I am currently using a Bayesian network model with 20 variables and 210 data points, with 15 locations measured at 14 different time points each. There are also some restrictions on what types of ...
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2answers
36 views

What model would be appropriate for predicting electrical consumption given multiple (mostly) independent variables?

I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including: daily temperature (continuous) hours ...
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21 views

Softmax regression or $K$ binary logistic regression

For a multi-class classification problem, we can use $K$ binary logistic classifiers, or one softmax regression classifier, so how to make the choice between the two? IMHO, the $K$ binary logistic ...
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8 views

Use a different loss function for cross validation in liblinear

I am trying to learn a L2 regularized Logistic regression model in liblinear. I need a way to specify the C parameter which I do by cross validation. However, the loss/accuracy measure in cross ...
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1answer
41 views

Approach for mapping consumer preferences

I have this web application where I need to map consumer preferences based on some input information and individual choices. My goal is to create a list of product recommendations and evaluate the ...
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7 views

Specifying the validation dataset for liblinear

I am trying to use the liblinear logistic regression model with L2 regularization. I don't want the training data to be splitted for the cross validation. I want to specify my own validation set for ...
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28 views

In Gaussian Processes, how to understand the hyper-parameters optimization?

I know that in a GP, hyper-parameters are optimized by maximizing the marginal likelihood. Could anyone explain this method to me please? Thanks for your help in advance.
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1answer
57 views

How to understand the log marginal likelihood of a Gaussian Process?

I'm trying to understand Gaussian Processes. Could anyone tell me: Why we need to use the log marginal likelihood? Why using log, the marginal likelihood can be decomposed to 3 terms (including a ...
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30 views

Interpretation of Linear SVM Coefficients [duplicate]

I’m building a model using Linear SVM from the Scikit-learn package in Python. I have found that Linear SVM performs much better on my training set than Logistic Regression. My question is, is there ...
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1answer
37 views

Meaning of latent features?

I'm trying to understand matrix factorization models for recommender systems and I always read 'latent features', but what does that mean? I know what a feature means for a training dataset but I'm ...
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4answers
117 views

Analysing a CV with machine learning

What would you use to categorise or rank a Resume/CV with machine learning? I would like to make particular CVs stand out to recruiters.
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16 views

A framework for comparing the performance of Expectation Maximization

I have my own implementation of the Expectation Maximization (EM) algorithm based on the following paper http://pdf.aminer.org/000/221/588/fuzzy_k_means_clustering_with_crisp_regions.pdf I would like ...
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1answer
90 views

What data mining/machine learning approach to use for a scoring model?

Suppose I have a large data set with lots of features(attributes). And I'm tasked to build some kind of scoring model to rank certain objects with all these features. How do I go about doing this? ...
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24 views

Method to select meaningful features for nearest neighbor classification

i try to perform some k nearest neighbor classification in R. That for i want to select the most meaningful features to deal with the curse of dimensionality. I have already decided to use Mahalanobis ...
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3answers
56 views

Recognition of simple patterns and prediction

I have been doing supervised learning and classification with multilayer perceptron for some time. But now I need to use unsupervised learning to infer the presence of a pattern and I would need some ...
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36 views

Squared Error vs Absolute Error loss functions [duplicate]

The two most popular types of loss functions are 1) squared error: $L(y,f(x))=(y-f(x))^2$ --> best estimate is the $E(Y|x) $ 2) absolute error: $L(y,f(x))=|y-f(x)|$ --> best estimate is the ...
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1answer
58 views

How to compare models from different but related datasets?

I'm building regression models on four the different but related data set and at the end, I want to test the significance of models. Since my models are built in a different data set, it's not ...
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54 views

Mixture of probits: understanding truncated-based likelihoods

I am trying to implement a mixture model of probits to infer the best decision boundary for every latent subpopulation. When doing Gibbs sampling, we eventually have to compute $P(y^* | w_c)$ where ...
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1answer
18 views

Regression Gaussian Estimates instead of points

I am using a support vector regression is order to get estimates of a variable y. I want to receive a probability distribution of my estimates and not just point estimates. I want to predict Gaussian ...
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1answer
47 views

When is it appropriate to use PCA as a preprocessing step?

I understand that PCA is used for dimensionality reduction to be able to plot datasets in 2D or 3D. But I have also seen people applying PCA as a preprocessing step in classification scenarios where ...
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18 views

After applying SVD, how do I find which features from my original dataset were most significant?

I'm using MathNet.Numerics library in C# to find the SVD but the Sigma matrix gives no indication of which values correspond to which features. It simply lists them in the most significant order. ...
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27 views

Calculating the variance of a model?

I often hear about the bias-variance tradeoff to evaluate classifiers. Now I want to calculate them. I often compute the AUC of a binary classifier to evaluate its performance and do a 10-fold ...
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43 views

Some doubt in reading Machine Learning A Probabilistic Perspective ( chapter 3.2 )

When I am reading Murphy's Machine Learning A Probabilistic Perspective. In chapter 3.2. I have some doubt. I think the author want to express is two things. First, we can use Bayes formula to ...
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1answer
88 views

What is the difference between a multi-label and a multi-class classification?

What is the difference between multi-label classification and multiclass classfication. Speficially, what is the difference between a label and a class? Please provide a clear example. "Multiclass ...
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26 views

Usefulness of Z-normalization in Machine Learning

Z-normalization means rescaling the feature $X$ by subtracting the average $\mu$ and dividing by its standard deviation $\sigma$, i.e., $(X-\mu)/\sigma$. What is the usefulness of normalizing data ...
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26 views

Binary classification of dated text documents with seasonality

I have a collection of training documents with publication dates, where each document is labeled as belonging (or not) to some topic T. I want to train a model that will predict for a new document ...
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3answers
383 views

How is cross validation different from data snooping?

I just finished "An Introduction to Statistical Learning". I wondered whether using cross-validation to find the best tuning parameters for various machine learning techniques is different from data ...
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1answer
84 views

Analysis of Customer satisfaction surveys

I have customer feedback data about 2-3 products from 100 customers. Number of questions are around 160. I have data in excel format. Header row contains the question and row below contains the ...
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67 views

Rademacher bounds for unbounded loss functions

All common treatment of PAC bounds based on Rademacher complexity assume a bounded loss function (for a self-contained treatemnt, see this handout by Schapire. However, I could not find any result for ...
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65 views

Duda, Hart, Stork No Free Lunch Discussion

Please see this question regarding Duda, Hart, and Stork's No Free Lunch Thm Discussion Hi all, I was having trouble understanding the description of the NFL theorem in Duda, Hart, and Stork. My ...
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1answer
42 views

Predicting the impact point of a moving object

Suppose we have a moving object (a horizontal projectile motion as one of the most basic examples). Is there any way to predict where it will hit finally? Please note that I'm looking for a machine ...
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21 views

How much time is reasonable for training Restricted Boltzmann Machine?

I have implemented Binary RBM in Matlab. I am using 60000 images as an input to train RBM. It takes approximately 11.3 minutes. I used tic and toc functions to evaluate above mentioned elapsed time. ...
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1answer
78 views

Variance-covariance matrix for ridge regression with stochastic $\lambda$

In ridge regression with design matrix $X$, outcomes $y$, fixed regularization parameter $\lambda$, and errors $\epsilon\sim\mathcal{N}(0, \sigma^2I)$, the computations for the ridge regression ...
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16 views

GB-RBM unable to learn and generate simple 2D-Motions?

I am trying to apply a GB-RBM to a variation of the LASA Handwriting Dataset to be able to generate new examples. My dataset contains motion trajectories of simple shapes like 'S' shapes, spirals, 'C' ...
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22 views

Bayesian Perceptron - how can I generate many different perceptrons?

I am going to implement the Bayesian version of a perceptron that I read in the Statistical Mechanics of learning, by Engel-Van Den Broeck. The idea to improve the performance is to use many Gibbs ...
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34 views

Conditional or Joint Probability under Various distributions

In various statistical models the baseline equation (like in Naive Bayes $$\mathrm{classify}(f_1,\dots,f_n) = \underset{c}{\operatorname{argmax}} \ p(C=c) \displaystyle\prod_{i=1}^n p(F_i=f_i\vert ...
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1answer
45 views

Where can I use kernels other than Gaussian (like Cauchy, laplacian) in kernel methods in machine learning? Or maybe in kernel density estimation?

In few papers I read that - kernel used doesn't really matter for kernel density estimation but bandwidth of the kernel is the most important factor. But I did not see any mathematical explanation to ...
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5 views

How accurate sum of kernel function needs to be, so that we can use it in Mean shift algorithm (may be for image segmentation)?

Mean shift is a procedure for locating the maxima of a density function given discrete data sampled from that function. It is useful for detecting the modes of this density. This is an iterative ...
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2answers
160 views

Ensembles of Ensembles?

I like the idea of ensemble learners, especially Bagging, but I always wondered as why they are not the most powerful learners since they have a clean motivation. I don't have the answer to that ...
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14 views

How to interpret merits in Weka with ChiSquaredAttributeEval and SVMAttributeEval?

I want to interpret the goodness of attributes using feature selection with 10-fold cross validation. With ChiSquared I get something like this (deletet attributes with merrit was 0 in all folds): ...
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22 views

Predict feature combination with highest probability

I trained a Support Vector Machine with the caret package in R. My dataset looks the following: ...
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18 views

Regression line fit for linearly increasing data with manual reset

I've a linearly increasing time series dataset of a sensor with value ranges between 50 to 150 on which I've implemented a simple linear regression algorithm to fit a regression line, and I'm ...
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14 views

Improving sentence segmentation in NLTK

I have been looking into problem of sentence segmentation lately. I have been referring to NLTK's book for this purpose. I followed their procedure to segment sentences presented here: ...
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30 views

What is the simplest way to classify airplan manuvers?

Suppose we have declared four motion types for air-plane. If we represent each maneuver with a trajectory line, what is the best classification method to retrieve the trajectory pattern with a similar ...