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

learn more… | top users | synonyms (1)

8
votes
0answers
362 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 ...
6
votes
0answers
800 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 ...
6
votes
0answers
87 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 ...
6
votes
0answers
860 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 ...
6
votes
0answers
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 ...
5
votes
0answers
80 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 ...
5
votes
0answers
105 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 ...
5
votes
0answers
415 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 ...
5
votes
0answers
169 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?
5
votes
0answers
210 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 ...
5
votes
0answers
492 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^{*} = ...
4
votes
0answers
65 views

Ideal statistical or machine learning technique to model highly cross-correlated data

I'm trying to build a model that can predict streamflow for an alpine (snowmelt-fed) watershed using snow albedo (roughly, the energy reflectance of the snow) data. Albedo controls the melt of the ...
4
votes
0answers
78 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 ...
4
votes
0answers
26 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 ...
4
votes
0answers
95 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 ...
4
votes
0answers
63 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 ...
4
votes
0answers
255 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 ...
4
votes
0answers
380 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 ...
4
votes
0answers
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 ...
4
votes
0answers
121 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 ...
4
votes
0answers
62 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 ...
4
votes
0answers
533 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. ...
4
votes
0answers
71 views

How to learn similarity of typed/attributed graphs?

I have a question for graph machine learning gurus :). For this project I'm working on, I need to be able to learn similarity between typed graphs. By typed I mean that every vertex and every edge of ...
4
votes
0answers
98 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 ...
4
votes
0answers
180 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 ...
3
votes
0answers
30 views

Markov decision process in R for a song suggestion software?

We have a music player that has different playlists and automatically suggests songs from the current playlist I'm in. What I want the program to learn is, that if I skip the song, it should decrease ...
3
votes
0answers
27 views

Variance of binomial vs. multinomial distribution in cross-validation

Suppose we have a dataset with $N=100$ observations. We do $K$-fold cross-validation with $K=10$ and $K=100$. In the first case, the classification decisions are sampled (can I say it like this?) ...
3
votes
0answers
21 views

Bound on the total change using Pearson's r

I am given an increasing series $(x_1,....x_n)$ and I know the pearson correlation between $(x_1,....x_n)$ and some (unknown) increasing series $(y_1,....y_n)$. Can I derive an upper and a lower ...
3
votes
0answers
44 views

Decision boundary in multivariate naive Bayes

This is from a sample exam for which I do not have the solutions. The question as stated is: True or False: The multivariate Gaussian naive Bayes always has a linear decision boundary. Explain ...
3
votes
0answers
29 views

Heuristics streaming data matching

I have an index composed by thousands of documents. Slightly modified copies of those documents are sent to my application in small chunks, and I need to check, from those chunks, which document has ...
3
votes
0answers
67 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 ...
3
votes
0answers
58 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 ...
3
votes
0answers
37 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 ...
3
votes
0answers
50 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 ...
3
votes
0answers
85 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 ...
3
votes
0answers
105 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 ...
3
votes
0answers
87 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 ...
3
votes
0answers
98 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
votes
0answers
87 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 ...
3
votes
0answers
105 views

Model selection in offline vs. online learning

I've been trying to learn more about online learning lately (it's absolutely fascinating!), and one theme that I haven't been able to get a good grasp on is how to think about model selection in ...
3
votes
0answers
174 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"). ...
3
votes
0answers
59 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 ...
3
votes
0answers
29 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 ...
3
votes
0answers
70 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 ...
3
votes
0answers
54 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 ...
3
votes
0answers
52 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 ...
3
votes
0answers
79 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 ...
3
votes
0answers
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 ...
3
votes
0answers
36 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 ...
3
votes
0answers
86 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 ...