Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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86 views

R language-statistics-significance testing [duplicate]

Possible Duplicate: R language and statistics hypergeometric test This is in reference to my first question, although I have got an answer for it from Spacedman. But I am not entirely satisfied ...
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0answers
744 views

What is the appropriate method to use to calculate customer lifetime value?

I'd like to figure out what the potential lifetime value of a customer may be based on their purchasing patterns with our products. I have transactional data that tells me what a customer purchased, ...
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2answers
322 views

Project management for remote collaboration in prediction

Are there any tools for remote collaboration in prediction or machine learning settings? I am looking for a computing environment that includes appropriate source control, keeps track of how ...
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0answers
725 views

How does one prove that a separating hyper-plane exists for a linearly separable pattern?

How does one prove that a separating hyper-plane that can be represented as a linear combination of the training samples exists for a linearly separable pattern? Although, it looks pretty obvious ...
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3answers
354 views

Do image recognition efforts always rely on machine learning and statistics?

This is something I've always wondered. Consider the Kinect. It takes its 3d image data and manages to recognize that a human is contained at a given boundary. Are these types of technologies ...
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3answers
24k views

Support vector regression for multivariate time series prediction

Has anyone attempted time series prediction using support vector regression? I understand support vector machines and partially understand support vector regression, but I don't understand how they ...
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0answers
113 views

Bayesian classifier and discovery of new classifications

I've written Naive Bayesian classifiers before, they work wonderfully. But I'd like a classifier which will learn like a Bayesian classifier and identify new classifications when a new cluster emerges....
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3answers
8k views

How well does R scale to text classification tasks? [closed]

I am trying to get upto speed with R. I eventually want to use R libraries for doing text classification. I was just wondering what people's experiences are with regard to R's scalability when it ...
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2answers
242 views

What techniques are used for empirical, stochastic simulation of a time series?

Suppose you have recorded a set of paths in the $y,t$ plane, with $y = f(t)$, $f$ is a stochastic function (i.e. there is a noise term), and $t$ might be time or some other monotonic increasing ...
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1answer
990 views

Gibbs sampling for a simple linear model — need help with the likelihood function

So in order to better acquaint myself with Gibbs sampling, I've been working on a fairly simple linear model, written in Python/R. Basically, I have 2-dimensional input data (the xi) and a scalar ...
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4answers
989 views

To what extent is the distinction between correlation and causation relevant to Google?

Context A popular question on this site is " What are common statistical sins?". One of the sins mentioned is assuming that "correlation implies causation..." link Then, in the comments with 5 ...
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1answer
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What is the $i$th sufficient statistic in the EM algorithm for Gaussian mixture models?

I am reading up on the EM algorithm for Gaussian Mixture Models, and there is consistent reference to the $i$th sufficient statistic. What is this, and why is it relevant to the algorithm?
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2answers
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What is the best way to learn the fundamentals of probability required for machine learning algorithms?

I took a probability course in university a few years ago, but I'm going through some machine learning algorithms now and some of the math is just befuddling. Specifically right now, I'm learning ...
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1answer
105 views

Training sample division vs extra binary/nominal factor

Suppose, there is some classification/regression problem. It seems for me, that sometimes division of training sample by some feature (or maybe by some other reasonable method, say feature generation ...
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1answer
336 views

Labels in the Digit1 dataset for semi-supervised learning

I am working with the "Digit1" dataset introduced by the book "Semi-Supervised Learning" by chapalle et. al, as one of the benchmark datasets in the field. In the dataset description located at: http:...
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6answers
24k views

Compare R-squared from two different Random Forest models

I'm using the randomForest package in R to develop a random forest model to try to explain a continuous outcome in a "wide" dataset with more predictors than samples. Specifically, I'm ...
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1answer
5k views

Text Classification in R [closed]

New to R, and am trying to do text classification. I am using R package tm to convert raw txt data into matrix. Here's the relevant code snippet. ...
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3answers
1k views

Software for drawing ROC curve

Having the sensitivity and specificity values, what software do you recommend that enables drawing the ...
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1answer
3k views

How to draw an ROC curve?

If I have values for sensitivity and specificity for a group of studies, for example like this: ...
8
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3answers
943 views

Feature construction in R

I am wondering if there are any algorithms (perhaps genetic algorithms) in R for feature construction (deriving candidate predictors from existing predictors)? I am thinking of a routine to test ...
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2answers
3k views

On the “strength” of weak learners

I have several closely-related questions regarding weak learners in ensemble learning (e.g. boosting). This may sound dumb, but what are the benefits of using weak as opposed to strong learners? (e.g....
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1answer
2k views

2D object recognition using MATLAB

Have you any idea about implementing 2D object recognition with MATLAB? Which characteristics of objects can feed a neural network? It's my training data-set (provided by ETH University of ...
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2answers
2k views

Feature selection for low probability event prediction

I'm currently trying to predict the probability for low probability events (~1%). I have large DB with ~200,000 vectors (~2000 plus examples) with ~200 features. I'm trying to find the the best ...
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2answers
364 views

Is there a generic term for measures of correctness like “precision” and “recall”?

Suppose I am building some predictive models and then creating a report detailing how "good" those models were in various ways. Is there a generic (maybe even non-technical) term for the various ...
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2answers
699 views

Incorporating seasonality into CART models

The problem I am trying to solve it predicting sales for an item for the next $n$ weeks. Obviously, seasonality is a major factor for such predictions. If we use a time series based model, then we ...
27
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2answers
6k views

Support vector machines and regression

There's already been an excellent discussion on how support vector machines handle classification, but I'm very confused about how support vector machines generalize to regression. Anyone care to ...
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3answers
2k views

Switching from unsupervised to supervised learning [closed]

Disclaimer: This is reposted from stackoverflow. I am working on a research-oriented system of collaborating agents. The agents perform many stochastic experiments (thousands per second), ...
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9answers
941 views

Book for broad and conceptual overview of statistical methods

I am very interested about the potential of statistical analysis for simulation/forecasting/function estimation, etc. However, I don't know much about it and my mathematical knowledge is still quite ...
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0answers
107 views

Problem on parametric learning when datasets are small

I'm currently writing a program to learn a TAN (Tree-Augmented Bayesian network) classifier from data, and I have almost finished it. I use the algorithm described in Friedman's paper 'Bayesian ...
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1answer
333 views

AdaBoost on a continuum of base classifiers

A tutorial on AdaBoost suggests that AdaBoost can be applied to a continuum of classifiers (at the bottom of the first page). Does it mean to simply discretize the classifiers, for example, which are ...
2
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1answer
151 views

Is it valid to assign observations partially to both test and train in n-fold cross validation?

I'm looking to use n-fold cross validation for selecting meta-parameters for fitting a model to a dataset. However, dropping observations entirely from the learning-set while fitting the model to each ...
2
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1answer
2k views

The upper bound of the training error of AdaBoost

I am reading an overview of AdaBoost written by Schapire, which calculates the upper bound of the training error in Eq. (5), section 3. In fact, it states that $$\prod_{t}Z_t=\prod_{t}\left[2\sqrt{\...
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3answers
2k views

Machine learning techniques for time series estimation - forecasting price

Can anyone recommend any machine learning techniques for time series estimation? I have a series of times $t_{1}...t_{n}$, each having a set of associated features $f_{1}...f_{m}$, and a value $x$. ...
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4answers
2k views

Are there cases where there is no optimal k in k-means?

This has been inside my mind for at least a few hours. I was trying to find an optimal k for the output from the k-means algorithm (with a cosine similarity metric) so I ended up plotting the ...
4
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6answers
3k views

Off the shelf tool for multi-label classification

I have a data set that has 10k documents, each of which is mapped to one and only one of 4k categories. This forms my training set. My requirement is that when a new, unseen document comes in, I ...
14
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4answers
925 views

How to begin reading about data mining?

I'm a novice who is going to start reading about data mining. I have basic knowledge of AI and statistics. Since many say that machine learning also plays an important role in data mining, is it ...
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1answer
2k views

Statistically comparing classifiers using only confusion matrix (or average accuracies)

Is it possible to perform a statistical test to determine if one classifier is better than the other using only the confusion matrices of these classifiers? What about the average accuracies from k-...
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3answers
37k views

What are the measure for accuracy of multilabel data?

Consider a scenario where you are provided with KnownLabel Matrix and PredictedLabel matrix. I would like to measure the goodness of the PredictedLabel matrix against the KnownLabel Matrix. But the ...
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2answers
5k views

Deep learning vs. Decision trees and boosting methods

I am looking for papers or texts that compare and discuss (either empirically or theoretically): Boosting and Decision trees algorithms such as Random Forests or AdaBoost, and GentleBoost applied to ...
2
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1answer
123 views

How to combine 2 different observations to improve state estimates?

Context Let $\mathbf{x}_i \in \mathbb{R}^{100}$ and $\mathbf{z}_i \in \mathbb{R}^{20}$ be input vectors with the same corresponding target $\mathbf{y}_i \in \mathbb{R}^{25}$. Using ridge regression we ...
25
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4answers
700 views

Addressing model uncertainty

I was wondering how the Bayesians in the CrossValidated community view the problem of model uncertainty and how they prefer to deal with it? I will try to pose my question in two parts: How important ...
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2answers
60k views

Measures of variable importance in random forests

I've been playing around with random forests for regression and am having difficulty working out exactly what the two measures of importance mean, and how they should be interpreted. The ...
3
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1answer
502 views

Kernel PCA vs. k-means

Could someone compare k-means and kernel PCA in the domain of vector quantization (memory, speed, effectivity, ...)?
4
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1answer
205 views

Supervised learning approaches which can accommodate a supervisory signal composed of multiple dependent continuous variables?

This is a supervised learning problem. Ideally would like to work in R due to having an easy way to pre-process the input data, but could work around that as well. For each sample, input consists of ...
2
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1answer
214 views

Variability of a curve with 4 parameters

Say I have 10,000 data in 2-D and I want to fit a curve to them. There are many functional forms this curve could take -- polynomial, B-spline, trigonometric, and so on. I've decided that I only want ...
38
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3answers
23k views

Creating a “certainty score” from the votes in random forests?

I am looking to train a classifier that will discriminate between Type A and Type B objects with a reasonably large training set ...
180
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3answers
86k views

Generative vs. discriminative

I know that generative means "based on $P(x,y)$" and discriminative means "based on $P(y|x)$," but I'm confused on several points: Wikipedia (+ many other hits on the web) classify things like SVMs ...
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3answers
5k views

Is it valid to select a model based upon AUC?

I have plot ROC for several models. These models were used to classify my samples into 2 classes. Using these commands, I can obtain sensitivity vs. specificity plots for each model: ...
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17answers
11k views

Machine learning cookbook / reference card / cheatsheet?

I find resources like the Probability and Statistics Cookbook and The R Reference Card for Data Mining incredibly useful. They obviously serve well as references but also help me to organize my ...
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2answers
10k views

How to correctly use the GPML Matlab code for an actual (non-demo) problem?

I have downloaded the most recent GPML Matlab code GPML Matlab code and I have read the documentation and ran the regression demo without any problems. However, I am having difficulty understanding ...

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