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

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Thesaurus for statistics and machine learning terms

Does there exist any reference thesaurus for statistics and machine learning terms? I know that Wikipedia articles often contain synonyms, but I would like to have a mere thesaurus that I could go ...
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744 views

Anomaly Detection with Dummy Features (and other Discrete/Categorical Features)

Intro This is my first time posting on here, so please, if anything doesn't seem technically correct, either in the formatting, or the use of correct definitions, I'm interested to know what ...
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349 views

Link Anomaly Detection in Temporal Network

I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
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262 views

Statistics for machine learning, papers to start?

I have a background in computer programming and elementary number theory, but no real statistics training, and have recently "discovered" that the amazing world of a whole range of techniques is ...
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825 views

Maximum entropy classifier and sentiment analysis

I am doing a project work in sentiment analysis (on Twitter data) using machine learning approach. In order to find the 'best' way to this I have experimented with naive Bayesian and maximum entropy ...
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165 views

Self-organizing maps: fuzzy input?

as my first post I would like to know if there are SOM implementations (preferably R) available that accept fuzzy input. That is, I have data in which some nominal features are spread out between a ...
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73 views

Expectation Maximization Clarification

I found very helpful tutorial regarding EM algorithm. The example and the picture from the tutorial is simply brilliant. Related question about calculating probabilities how does expectation ...
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103 views

Compressed sensing: Optimization in $L_1$ norm and total variation with fourier coefficients

I'm reading the article Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information (Candes, Romberg and Tao, 2004). In this article they are talking ...
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166 views

Under which conditions do gradient boosting machines outperform random forests?

Can Friedman's gradient boosting machine achieve better performance than random forests? If so, in which conditions or what kind of data set can make gbm better?
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200 views

How to combine multiple similarity measures?

I have a hyperspectral image where the pixels are 21 channels. So each pixel $\in \mathbb{R}^{21}$. I want to perform clustering on the pixels with similarity defined by two different measures, one ...
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479 views

Manifold regularization using laplacian graph in SVM

I'm trying implement Manifold Regularization in Support Vector Machines (SVMs) in Matlab. I'm following the instructions in the paper by Belkin et al.(2006), there's the equation in it: $f^{*} = ...
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85 views

Open-sourced pairwise learning models

I am solving classification problem using pairwise-learning training set. We have 2 classes: bad and good. We also have pairs of objects $(a_i,b_i)_{i=1}^n$, meaning that object $a_i$ is better than ...
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72 views

Are all models useless? Is any exact model possible — or useful?

This question has been festering in my mind for over a month. The February 2015 issue of Amstat News contains an article by Berkeley Professor Mark van der Laan that scolds people for using inexact ...
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23 views

VC dimension of regression models

In the lecture series Learning from Data, the professor mentions that the VC dimension measures the model complexity on how many points a given model can shatter. So this works perfectly well for ...
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93 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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222 views

How to perform hypothesis testing for comparing different classifiers

I am trying to classify a small dataset (around 500 records) into two classes. I used various methods like SVM, Naive Bayes and k-nn classifier. Now, I would like to set the results from one of the ...
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355 views

Evaluating a regression model's performance using training and test sets?

I often hear about evaluating a classification model's performance by holding out the test set and training a model on the training set. Then creating 2 vectors, one for the predicted values and one ...
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1k views

Confusion with Vowpal Wabbit's multiple-pass behavior when performing ridge-regression

I have encountered many peculiarities/misunderstandings of Vowpal Wabbit when trying to do online multiple-pass learning. Specifically, I need to solve a Ridge Linear regression problem, with ...
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119 views

What kind of plot am I looking at?

I stumbled on to these following two slides (slides 21 & 22 on a machine learning tutorial found here): The first is obviously an $x,y$ scatterplot of height and weight. But what is the ...
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366 views

Choosing an appropriate minibatch size for stochastic gradient descent (SGD)

Is there any literature that examines the choice of minibatch size when performing stochastic gradient descent? In my experience, it seems to be an empirical choice, usually found via ...
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61 views

How to model the distribution of a word game in order to find correlation between demographics and chosen words

I have an experiment (in the form of a word game) whereby people are asked to choose a set of words to describe associations with a topic with the aim of having another person guess the topic. I ...
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509 views

Post processing random forests using regularised regression: what about bias?

I have been playing around with post processing the results of the random forest for regression machine learning algorithm in order to try and do better than the default mean of all trees prediction. ...
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97 views

Graphical nominal model

Suppose I have a set of $k$ matrices. $$ \mathbb D = A_1,A_2,...,A_k $$ Each column of $A$ is categorical vector. $$ A = v_1,v_2,...,v_n $$ I want to find the mapping $$ f: A ...
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176 views

Category selection for text classification

It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to ...
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65 views

Need pointers to deep learning tutorials

I'm looking for good study material about deep belief networks, with particular emphasis to classification and feature extraction tasks for non-image data. I don't seem to find a great deal about ...
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56 views

State of the Art versions of Generalized Additive Models

Generalized Additive Models [Tribshirani 86] was well received with over 1335 Citations. I am also aware of the popular version of GAM - the Multivariate Adaptive Regression Splines [MARS by Friedman ...
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36 views

Is it possible for a reinforcement learning agent to create or generate additional features

I have little background knowledge of Machine Learning, so please forgive me if my question seems silly. Based on what I've read, the best model-free reinforcement learning algorithm to this date is ...
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45 views

Bounding the expectation of the difference between empirical vs generalization error

Let the (defect) difference between empirical and generalization error be: $$D[f_S] = I_S[f_S] - I[f_S]$$ where the empirical risk is: $$I_S[f_S] = \frac{1}{n}\sum^n_{i=1} V(f_S,z_i)$$ where ...
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72 views

Explanation for large difference in SVM and Naive bayes results

I have a dataset with 389 data evenly distributed into 6 classes. Each data has 1024 features. So my dimension is much larger than my observation data. I have tried to see some common classifiers on ...
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90 views

Connections between d' (d-prime) and AUC (Area Under the ROC Curve); underlying assumptions

In machine learning we may use the area under the ROC curve (often abbreviated AUC, or AUROC) to summarise how well a system can discriminate between two categories. In signal detection theory often ...
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69 views

Convolutional neural networks: Aren't the central neurons over-represented in the output?

[This question was also posed at stack overflow] The question in short I'm studying convolutional neural networks, and I believe that these networks do not treat every input neuron ...
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88 views

What enforces features diversity in RBM?

I'm working on an implementation of a Restricted Boltzman Machine (RBM). I made some tests on the MNIST dataset trying to learn a representation of the digit 2. My inputs are binary images. My aim is ...
3
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85 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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167 views

Generalized linear model with lasso regularization for continuous non-negative response

I have a big data problem with a large number of predictors and a non-negative response (time until inspection). For a full model I would use a glm with Gamma distributed response (link="log"). ...
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61 views

Vapniks proof of the basic lemma

In his book Statistical Learning Theory (1998), Vladimir Vapnik proves an inequality needed to prove a bound on the risk for indicator loss functions. Theorem 4.1 on page 133 he derives the following ...
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58 views

What is the posterior probability of the data given the model used for model averaging with Bayesian Logistic Regression?

I am trying to learn about Bayesian Model Averaging using Bayesian Logistic Regression (Genkin, A., Lewis, D. D., & Madigan, D. (2007). Large-scale Bayesian logistic regression for text ...
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28 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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64 views

How to estimate False Discovery Rate from p-value distribution?

I have learned many models and I calculated p-values for the cross-validation errors. I want to select significant models based on the false discovery rate (FDR). How can I estimate the FDR from ...
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53 views

Machine learning with ordered labels

The usual method for adapting binary classifiers like various SVMs to multilabel data is one-vs-all, which assumes that labels are independent and in case of a prediction error we don't care what ...
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51 views

Reconstruct a “blocky” picture?

Consider a finite set $A$. Let the sample space be $A\times A$. We have an unknown probability distribution $f$ on this sample space. Now this probability distribution has a "blocky" property, which I ...
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78 views

Standard deviation in regression trees

In a regression tree, it is often assumed that each leaf is a Gaussian distribution $\mathcal{N}(\mu_i, \sigma)$, where $i$ is the index the leaf. Is $\sigma$ calculated as the standard deviation ...
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1k views

Is R-squared value appropriate for comparing models?

I'm trying to identify the best model to predict the prices of automobiles, using the prices and features available on automobile classified advertisement sites. For this I used couple a of models ...
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35 views

Selecting problems of the appropriate difficulty based for adaptive learning

I'm currently working on an adaptive learning system for high school maths. Students complete questions in quizzes and I need to be able to select questions of the appropriate difficulty level (say ...
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83 views

Quality of a model and the bias-variance tradeoff

Take linear regression as the example, given one specific data set $D_1=\{(x_1,y_1),...(x_n,y_n)\}$, we could train a model with one specific parameter estimate $\hat\theta_1$, if we do the training ...
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104 views

Proof of Theorem 7.3 in book of Probabilistic Graphical Models by Daphne Koller

I'm studying graphical models myself and reading contents about bayesian networks. When I am reading in page 371, section 8.1.4 Linear-Gaussian models, in Pattern Recognition and Machine Learning, I ...
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93 views

Mutual information/pointwise mutual information for measuring prediction

I want to measure how well I predict a vector $Y$ (vector not a label) for observation $X$. Both $X$ and $Y$ have the same set of features ($1\times n$). For that, I thought of "scoring" the ...
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497 views

How to think of features in NLP problems

I am working on a Named Entity Recognition (NER) project. Instead of using an existing library, I decided to implement one from scratch because I wanna learn the basics of how PGMs work under the ...
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102 views

Concerns about housing data mining

Can anyone suggest me good papers related to housing data mining. I have googled for a while and couldn't find any recent paper. I am assuming that people have stopped doing any mining related to ...
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133 views

Linear Discriminant Analysis: Using subject as classification

I have a problem where I need to identify from which subject a particular set of data points came. More specifically, my problem is that I need to demonstrate that my subjects (N=9) can be ...
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293 views

Prediction using SVD and Fisher's linear discriminant

Where can I get an explanation of the procedure used when making a prediction using SVD? Let me elaborate a bit more. Suppose you have data in a matrix $A$ containing two classes. In particular, you ...