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

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Expected required sample length to train a hidden Markov model

Say one wishes to train a hidden Markov model with $n$ hidden states, and (accidentally) the problem itself can be described with a hidden Markov model with $n$ (or less states). What is the expected ...
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12 views

k-means clustering on percentages

Can we do k-means clustering on percentage data (like 56%, 44%, 22%, 13%, etc.)? There is a data set, and data in various parts are measured in percentages.
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1answer
8 views

Assumption behind few latent features in recommender systems?

I know in recommender systems you have a rating matrix and then you factorize this matrix into two matrices and then learn those matrices with gradient descent. In those matrices we specify the number ...
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2answers
25 views

In Naive Bayes, why bother with Laplacian smoothing when we have unknown words in the test set?

I was reading over Naive Bayes Classification today. I read, under the heading of Parameter Estimation with add 1 smoothing: "Let $c$ refer to a class (such as Positive or Negative), and let $w$ ...
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1answer
35 views

How to decide which penalty measure to use ? any general guidelines or thumb rules out of textbook

A number of regularization measures are available in literatures, which is kind of confusing to beginners. The classical penalty is ridge by Hoerl & Kennard (1970,Technometrics 12, 55–67). ...
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1answer
48 views

How do you validate your machine learning models?

I am wondering what approaches are commonly used for validating a classification or prediction models: Approaches that am using at the moment: Using truth-sets: - ROCs, Bootstrapping, Accuracy, ...
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11 views

Forensics in wireless networks, anomaly detection and beyond?

first i'de like to apologize if this is not the right place. Next year i'm gonna be working on my final project in computer security, i have to build a wireless forensics tool that can analyse a data ...
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1answer
18 views

Feature Selection - Mutual Information with response variable that takes three values

I am trying to calculate Mutual Information scores for Feature Selection. I have successfully implemented the Mutual Information to test each feature against the binary response variable. Each ...
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1answer
23 views

Interpretation C value in linear SVM

My C value is very low (close to 0). Does this mean that my feature (dimensions) have no real separative (and thus predictive) value? (As the SVM basically chooses to ignore the training data ...
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1answer
45 views

Simple SVM Question

For a linear SVM, the documentation tells me the formula is: $$ \frac{1}{2}w^Tw+C\sum\limits_{i=1}^l\xi_i$$ Please explain to me in layman's terms what w (and ξ) represent. Is w the distance to the ...
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24 views

large variables and low sample (p > n) problem: ridge , LASSO, PLS, PCR which is most suitable for predictions

I am trying see whether to go for ridge regression, LASSO or principal component regression (PCR) or Partial Least Squares (PLS) in a situation where there are large number of variables / features (p) ...
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13 views

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
23 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
35 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

Why do we need nested cross-validation for parameter selection?

A normal cross-validation finds the best parameters such that: for each parameters: cross_validate(algorithm(parameters)) model = Use Best Model Why do ...
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7 views

FNN package for R - possible to use manhattan or other distance metrics? [on hold]

It is possible to change the algorithm, but I am stuck trying to change the distance metric. I know it usually makes little difference, but in this case I need it!
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10 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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60 views
+50

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
24 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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30 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
30 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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13 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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7 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
27 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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6 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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25 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
48 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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28 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
36 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
102 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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13 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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9 views

Which classifier is the best for short text classification, Naive Bays or RBFN? [on hold]

I am doing my M.E. project on short text classification for Online Social Networks.I am using Weka for classification.Please tell me between Naive Bays and RBFN which classifier is the good one for ...
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11 views

Naive Bays classifier showing results for precision, recall and F value is always 1

I am implementing Naive Bays text classifier using Weka. I have trained it with very few words (about 20). I am getting the result that precision, recall and f value all as 1. Is this possible? ...
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1answer
58 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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17 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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43 views

What are the currently hot clustering techniques? [closed]

I need a heads up on the most recent, hot clustering techniques out there to keep updated. Any links and/or buzzwords are welcome! I am aiming at protein sequences, but interested to get a general ...
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3answers
51 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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20 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
47 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 ...
2
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0answers
46 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 ...
3
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1answer
36 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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13 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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0answers
34 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 ...
2
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1answer
75 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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25 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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20 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
311 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
66 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 ...