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Statistical learning theory

I am looking for some good books for statistical learning theory. For an introduction I went through "An elementary introduction statistical learning theory Kulakrani" It was a good read with less ...
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1answer
32 views

How does explaining away cause problems for learning?

In one of his lectures Geoff Hinton explains that a big problem of sigmoid belief nets is the explaining away phenomenon. I didn't fully understand this. I see that the induced width of the graph ...
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0answers
13 views

What classifier to choose?

Hi all I am doing a supervised classification of a binary labelled data where I have a mixture of categorical and continuous attributes. I have in total 6 attributes and nearly 6500 instances. I was ...
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1answer
24 views

A/B split/bucket testing with three or more variants

Lets say I have three search engine e.g. search engine A, search engine B and search engine C. Each search engine is given a set of queries Q (e.g. apple,banana,carrot....), this set Q remains the ...
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1answer
22 views

Noisy label as a semi supervised

Hi I was wondering if we can compare Noisy label problem to a semi supervised approach? Also are there any papers on learning with noisy labels? Any help is appreciated.
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0answers
21 views

Learning theory for search data?

Has learning theory ever been applied in practice for search log data? If so, what are some findings about generalization/learnability from this data? I'm interested in generalization about an ...
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1answer
45 views

What are the null and alternate hypotheses? Will you use a left, right, or two-tailed test?

Ben has a coin which he claims is weighted in a way so that when he flips it, heads appears more often than 50% of the time. He tries to prove it to you by flipping the coin 100 times, which ...
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0answers
69 views

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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2answers
60 views

Singular covariance matrix in exploratory Factor Analysis

I'm kind of a noob to EFA and am trying to use the FANode object in Python. This is from the MDP library. I am using it on survey data to see which variables are tied together. Whenever I run it on my ...
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1answer
25 views

Is system identification and learning examples of parametric or nonparametric methods?

Difference between parametric and nonparametric statistical tests and What is the difference between distribution free statistics/methods and non-parametric statistics? is a good read for beginners. ...
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0answers
23 views

Good way to use adaptive learning rates in neural network

Adaptive learning rates means using different learning rate for different weight in neural network. Except for the emperical method which updates these learning rates based on consistency in gradient, ...
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2answers
75 views

How to learn the points inside a square with its boundary?

Is it possible to classify the points inside a square ? i.e. if $a \le x \le b$ and $c \le y \le d$ then label is $+1$ otherwise $0$. Is that possible using SVMs for example ? Thanks, Zach
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0answers
35 views

I need an insight on result of my analysis

I need some help/insights on result of my data analysis. My object is to classify 3 types of different numbers. ie) 1 or 2 or 3 I built C5.0 tree + leave group out cross validation (hold out) ...
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0answers
17 views

Trouble with perceptron algorithm?

I'm having issues implementing a perceptron algorithm. I grab what I call a "seed" hyperplane for my data. That is, one I've calculated that somewhat separates the data correctly. In my case, this ...
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0answers
30 views

Why do I get clipping when using tanh activation function?

I implemented a rather simple MLP NN as a part of my project. I'm just testing it on a sinusoid right now and I expect this network's output to follow the sinusoid without issues. Some network ...
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0answers
29 views

Metric optimization on discrete learning sample

There are a set of ("artifical") not Minkovski (triangle inequality is not guaranteed) metrics defined on set of objects. There are one etalon ("natural") metric, which estimation is known only for ...
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2answers
198 views

How much does it matter if my Masters is in Stats or Math (in Stats track)

I have a choice of Master's programs in statistics, one of which is formally a program in applied statistics, the other is formally in math with an applied statistics "track". The courses in the 2 ...
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3answers
211 views

Easy book to understanding basic concepts

I have a medium-strong background on programming and logic, however I'm trying to start using R, and other tools to make machine learning based studies of some problems. I did take probability and ...
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1answer
448 views

Question on leave one out and stratified 10-fold cross validation

I am confused with the answers to the questions below Assume that we have a dataset D with 100 examples, 50 of which belong to the class ’good’ and 50 belong to the class ‘poor’. Assume further that ...
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2answers
128 views

On the hardness of data to learn

Almost in all texts which are discussing theorems of statistical learning, they assume analyzing arbitrary unknown distribution (the worst case). But in practice different problems (different data) ...
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2answers
59 views

Terminology problem: “model selection” is the same as traning ?

In machine learning we have the following problem: Choosing the optimal model (or training): $$ f^* = \arg\min_{f \in \mathcal{F}} \sum_i l(x_i,y_i) $$ Is the term ...
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2answers
214 views

Learning probability bad reasoning. Conditional and unconditional

I have a problem, I'm learning probability at the moment (I'm a programmer) and starting I have this: (Source: Minka.) My neighbor has two children. Assuming that the gender of a child is like a ...
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1answer
89 views

PAC learning theory and lower bound on the amount of input samples

I am trying to answer the following question: "How much (binary) data do I need for my learner to have seen every variable of the dataset at least once?" In my set-up I am feeding my algorithm binary ...
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1answer
72 views

Learning an interpretable model

I am working on problems in the field of medical imaging where the need for a simple and interpretable model is important from a clinical perspective. This means that I have to explain the algorithm's ...
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0answers
52 views

Best model for many independent variables

Let's assume I don't know of the existence of clocks, so I want to build a model to predict the current time of day based on a large amount of other things I can measure, for example pressure, ...
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1answer
127 views

Statistical learning theory VS computational learning theory?

What relations and differences are between statistical learning theory and computational learning theory? Are they about the same topic? Solve the same problems, and use the same methods? For ...
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0answers
54 views

Kalman- Bucy filter: prior mean change

I have a question on Kalman-Bucy filter: the prior distribution is $g \sim N(0,σ_g^2 )$, signal is $ds=(μ+g_t )dt+σdZ_t$, posterior distribution becomes $g_t \sim N((\hat{g_t},\hatσ_t^2)$. ...
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1answer
50 views

When to normalize learning?

I'm trying to determine the effect of three types of learning on a group of subjects. I have their pretest scores and posttest scores. The current goal is to determine which intervention reduce the ...
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2answers
821 views

Estimating the covariance posterior distribution of a multivariate gaussian

I need to "learn" the distribution of a bivariate gaussian with few samples, but a good hypothesis on the prior distribution, so I would like to use the bayesian approach. I defined my prior: $$ ...
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2answers
236 views

Recommendations for learning probability and Bayesian statistics? [duplicate]

I have been very interested lately in learning Bayesian Statistics, but I have only a little bit of background in the frequentist statistics, only one term at University. Some of the books that I ...
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0answers
68 views

Is it advantageous to use dummy variables when learning a regression model?

Say I have 3 random variables ${X, Y, Z}$, and I have collected an iid sample of size $N$ from them: ${\cal D} = \{ (x_i, y_i, z_i), i = 1,\dots,N \}$. The conditional expectation $E[ X | y ]$ can ...
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1answer
282 views

Sparse representations for denoising problems

I have read in a huge number of papers that sparse models (sparse coding, dictionary learning, sparse matrix factorization, ...) are good solutions for image denoising problems. I know that ...
2
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0answers
47 views

Generalization error for classification with a nonconvex loss function

I've been working my way through Vapnik's 1998 Statistical Learning Theory book and one thing that I'm still unsure of is if his risk bounds hold for nonconvex loss functions -- i.e., when we can't be ...
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1answer
123 views

Mixed effect and learning curves

My question might be too simple but I just started to do statistical analysis and use R and is not always simple! I have performance data for 5 subjects with different level of experience, repeating ...
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2answers
267 views

Sources for learning (not just implementing) statistics/math through R

I am interested in examples of sources (R code, R packages, books, book chapters, articles, links etc) for learning statistical and mathematical concepts through R (it could also be through other ...
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2answers
187 views

Data analysis “quizzes” [duplicate]

Possible Duplicate: Locating freely available data samples Sites for predictive modeling competitions Are there sites or sources of "datasets" (either artificially created or taken from ...
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1answer
477 views

Evaluation of classifiers: learning curves vs ROC curves

I would like to compare 2 different classifiers for a multiclass text classification problem that use large training datasets. I am doubting whether I should use ROC curves or learning curves to ...
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4answers
3k views

Is a strong background in maths a total requisite for ML?

I'm starting to want to advance my own skillset and I've always been fascinated by machine learning. However, six years ago instead of pursuing this I decided to take a completely unrelated degree to ...
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1answer
179 views

How to determine the best number of weak classifiers to use in adaboost without overfitting the data

I was thinking by using validation but not quite sure how to go with it. Please list some papers or ideas on how. This is for multiclass problem (using one vs all approach). I think each ...
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6answers
403 views

Recommend an enjoyable / introductory book on Statistics [duplicate]

Possible Duplicate: A resource on concepts underlying statistics, not the techniques used in applied stats I am interested in learning more about Statistics and when I ran a Google / Amazon ...
2
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1answer
143 views

Under what conditions can a PLS regression model be expressed by single linear equation?

I am confused by two, yet inconsistent for me, facts: Since the PLS regression is expressed by matrices of scores and loadings as $$X=TP^T+E\\Y=UQ^T+F$$ how it can be translated into linear equation ...
3
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2answers
190 views

Goals for students in an introductory course

I am studying Statistics for business at introductory level, and having difficulty in handling the amount of information, partly because I am coming back to study after 5 years and I didn't do much ...
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0answers
105 views

The genuine inventors of concepts

General: It is almost a permanent need in researching progress addressing/referring the first inventor, proposer or developer of the concepts which are widely being used these days. Have got no clear ...
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2answers
450 views

Training of a Neural Network

I am trying to train an Artificial Neural Network for classification. In the input layers, I have 402 neurons; the first 400 are binary, and the last two are floating points in the range -1 to 1. In ...
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3answers
1k views

Online material to learn time series analysis

My question is if there are any good online materials for learning this. Something that introduces things well, especially ARMA models and the related math. Edit: I'm looking for something of the ...
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3answers
253 views

Learning on huge datasets

Basically, there are two common ways to learn against huge datasets (when you're confronted by time/space restrictions): Cheating :) - use just a "manageable" subset for training. The loss of ...
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6answers
4k views

Can you recommend a book to read before Elements of Statistical Learning?

Based on this post, http://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets, I want to digest Elements of Statistical Learning. ...
4
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1answer
147 views

When is there a representer theorem?

The case of regularization in a hilbert space is considered---an optimization problem with an error term and a Tikhonov-regularizer. In the article "When is there a representer theorem" it is stated ...
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2answers
287 views

How to figure out the behind concept of statistical-look problems?

The following is my experience doing some researches linked to statistical concepts. In the most situations I had no idea on how to organize the questions, ...
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4answers
360 views

How to digest statistical context?

Firstly, I suppose that not all active members of this interesting site are statisticians as their job. Otherwise the question being asked as follows does not make any sense! I respect them of course. ...