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8 views

Attribute value pair - matching with machine learning

I am totally new to machine learning area. I am trying to understand one type of question that I have been thinking of how to do since awhile but could not be able to wrap my head around with a ...
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1answer
56 views

Batch Active Learning for Classification

Say we have unlimited unlabeled data and we can ask an oracle for labels. We can use active learning to choose the most informative data samples for labeling, thus minimizing data labeling cost. If we ...
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0answers
37 views

Learning a transition function of a Markov decision process using infrequently sampled data

I have a dataset of battery trials, where a battery has been charged and discharged in different regimes, and its capacity was measured periodically to reflect the effect of the charging and ...
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2answers
44 views

perfect feature extraction doesn't exist?

Anyone is aware of any proofs that "perfect feature extraction doesn't exist" (on any domain, language, vision, etc). Either philosophical or mathematical, are fine.
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1answer
66 views

Algorithm to select predictors in Logistic Regression

I need to find a computer-driven way to come up with a model in logistic regression for an exploratory study using R. Usually I would just use the leaps or bestglm packages to find the best subset, ...
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2answers
63 views

R Caret - Repeated CrossValidation, finalModel and ROC curves

I got a problem understanding the meaning of the finalModel when using a repeated CV. ...
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2answers
35 views

How to find similar kind of project specification using Clustering Algorithm?

I have budget estimation of some bio-medical projects and their specification details. Could any one suggest me how to do clustering algorithm to find the similar kind of specification. Which ...
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0answers
19 views

measure the convergence rate of SVM under cross-validation

I want to measure the convergence rate of my SVM based classifier, i,e how many data points are necessary to build an accurate classifier. I have an initial learning set from which I remove X% (X from ...
3
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1answer
82 views

What are the Fundamentals of Applied Statistics?

Would the knowledge of the following topics suffice for a crash course on fundamentals of applied statistics? Basics of Probability Descriptive Statistics Estimation Theory Hypothesis Testing The ...
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1answer
41 views

Recognition of digit of size other than those in the training set using DNNs

I have a DNN trained on MNIST data (image 1 for digit '4') that recognizes images from the test set with high precision. Each digit is centered and all of them are roughly of the equal size. Will it ...
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0answers
33 views

How do the following three courses on statistical learning theory differ

I have no prior knowledge about statistical learning theory so I have no idea how broad this discipline is. Doing a search online I found Stanford, MIT and ETH Zurich all have this course with its ...
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0answers
29 views

Uplift Model in Matlab

I was wondering if anyone had an idea how to implement an uplift model in Matlab. I have data for marketing campaign a company I am studying ran. I would like to test the impact of the marketing ...
0
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1answer
59 views

Which are the topics in machine learning that a newbie have to pick up if he is interested in the area of pattern recognition?

This question is asked by a newbie who is interested in machine learning but is absolutely new to it. ML is a very wide topic and it is too much for a newbie to learn everything, then start focusing ...
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0answers
15 views

Identifying Changepoints in Learning Data

I have a "changepoint" problem that I do not understand. The data for my MWE comes from one human player as s/he played the same video game for 31 one-hour sessions (no more than one session per ...
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1answer
34 views

what is the relationship between covariance matrix and its variance parameter in linear mixed model

In parameter estimation for linear mixed model for unknown variance, I met some statements saying that "we assume G (as variance) is only known up to its variance ...
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0answers
52 views

How to find parameters by output

I'm new to machine learning. I have a problem like this - i have an object O with many parameters, say 30 (a1 - a30). Each parameter is integer 0 <= N <= 100. O is being sent to a function F, ...
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0answers
37 views

Why is VC dimension important?

Wikipedia says that VC dimension is the cardinality of the largest set of points that a algorithm can shatter. For instance, a linear classifier has a cardinality n+1. My question is why do we care? ...
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0answers
126 views

Background required for understanding “Elements of Statistical Learning” [closed]

What background knowledge is required for understanding "Elements of Statistical Learning"? Is there more approachable book which covers similar topics"? I have considered following books: 1) ...
3
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0answers
168 views

nnet: Decay vs Learning Rate

I am dealing with the nnet package in R. I know that the momentum $\alpha$ is used to decrease the fluctuations in weight changes over consecutive iterations. The ...
0
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1answer
210 views

Setting random seed for neural network

I'm training a neural network for a particular problem which can be predicted with 100% accuracy. However, the problem is that results tend to vary between 99% and 100% even if I train 10 different ...
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0answers
102 views

Speech recognition using baum welch and GMMs

I have been working on a speech recognition program for a while now, implemented in java, it uses HMMs as structure model and Baum welch for training using gaussian mixture models. Features are ...
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0answers
73 views

Forming a likelihood (and/or conditional distribution of a random variable) from an implicit function

EDIT: This is now written in a more minimalist form for statistics/econometrics audience rather than the more Bayesian learning or Bayesian Nash Equililbrium style. My problem involves finding the ...
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0answers
6 views

training/learning a function from domain and range alone?

Usually in supervised learning we have a data set $D$, which informs you for a certain input $x_i$ we expect a certain corresponding output $y_i$, and the appropriate function $f$ is trained by ...
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0answers
42 views

suitable flexibility of statistical learning method and number of predictors and number of samples

Out of a flexible and an inflexible statistical learning method, which one is expected to perform better when the number of samples is extremely large and the number of predictors is small? the ...
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0answers
55 views

Perceptron Learning Algorithm: what is the probability that the viewed data is linearly separable, after some number of steps?

My understanding is that the PCA: will not converge if the data is not linearly separable might take exponentially many iterations, even if the data is linearly separable I'm wondering if, after ...
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1answer
374 views

How does linear SVMs function in multi dimensional feature space

Can someone please explain me how does linear SVMs function in multi dimensional feature space ? I'm not able to picture how a linear SVM can perform classification in more than 2 dimensions. Also, ...
0
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0answers
26 views

Multi-label multinomial logistic regression

I have stock data with about 50000 features and 20 labels. Each of the label can take one of three values: -1, 0, 1. I've divided the data in 9:1 ratio so that nine tenth of the data is used to ...
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0answers
26 views

VC Dimension of the set of canonical hyperplanes

This is a proof of the theorem about VC Dimension of the set of canonical hyperplanes from Professor Mohri's lecture slide. I'm having difficulty with understanding the inequality$$ \forall i \in ...
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1answer
46 views

Automation level calculation

Suppose you have an end to end process which contains a series of tasks to be completed. You also have process automated at certain tasks but the rest of the tasks are completely manual. Also, these ...
0
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1answer
62 views

Independence of data points assumption

While reading an ML book, I realized that most of the time, the input data points are correlated with each other, and hence their observation is not independent. But then, why do we assume that the ...
2
votes
1answer
81 views

Why do we have to be concerned about the problem of overfitting on the training set?

For a hypothesis set $H=\{h_1,...,h_M\}$, randomly sampled training set $D_{train}$, and a learned hypothesis $g$ using $D_{train}$, the VC-bound of a finite hypothesis set tells us $$ ...
12
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1answer
281 views

In statistical learning theory, isn't there a problem of overfitting on a test set?

Let's consider the problem about classifying the MNIST dataset. According to Yann LeCun's MNIST Webpage, 'Ciresan et al.' got 0.23% error rate on MNIST test set using Convolutional Neural Network. ...
3
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1answer
79 views

In learning theory, why can't we bound like $P[|E_{in}(g)-E_{out}(g)|>\epsilon] \leq 2e^{-2\epsilon^{2}N}$?($g$ is our learned hypothesis)

Given Data $D_{in}$, number of data $N=|D_{in}|$, and hypothesis set $H=\{h_1,h_2, ...,h_M\}$. For a fixed hypothesis $h$, for example $h_1$, we can derive ...
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1answer
218 views

What does VC dimension tell us about deep learning?

In basic machine learning we are taught the following "rules of thumb": a) the size of your data should be at least 10 times the size of the VC dimension of your hypothesis set b) a neural network ...
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2answers
77 views

Mixed model for learning data

I’m working with data from a learning experiment in birds and I have some doubts I hope you can help me clarify. I'm interested in comparing the performance in a learning task between male and female ...
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1answer
83 views

Classifier for weighted class label

Is there any rule-based classifier which be able to classify samples with weighted class labels. In other word, different confidence in tagging samples. My problem deals with learning samples from ...
2
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1answer
31 views

Information content of a set of random variables

Suppose there is some distribution $F$ not known to us. However, we can get information about this distribution by means of samples, i.e. we have a set of random variables from this distribution. ...
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0answers
82 views

Remove column which has weak correlation between other

I have data set which contains 12 feature columns. The last one has Pearson correlations between 0.001 and 0.09. For Spearman and Kendall correlations are worth. Can improve the performance of ...
1
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1answer
165 views

Nonlinear Autoregressive model parameter estimation from time series

I'm working on a nonlinear multivariate autoregressive model of order 1 (markovian). It is a discrete-time dynamical system which models exchange of mass between compartments in a compartmental model ...
0
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1answer
25 views

Viability of software dev - Use of and requirements of NN

Hello I would like to know this two things regarding the viability of producting a software, so: 1) Are available on internet some OCR libraries for free? Can I train my own NN having only a laptop? ...
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2answers
221 views

Mixing proportion $\pi$ in Mixtures of Gaussians

I am trying to understand a little better mixtures of Gaussians and their generative approach in general. For a mixture of Gaussians we start with this formula: $$p(x)=\sum_{k=1}^{K}\pi_{k}\cdot ...
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0answers
18 views

What can I possibly get from my data to find what I need?

I am an end-user of a program. This program returns an output based on an input (a file + a database). I have made a few tests with differents files and databases, then constructed a CSV file of the ...
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0answers
40 views

Normal learning - multiple signals

I'm having trouble with the exercise below. I know that $E(η_t|z_t)= E(η_t) + [Cov(η_t,z_t)/Var(z_t)](z_t - E(z_t)) $ but still can't show 'b'. I imagine I'm missing something very simple... Can ...
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1answer
307 views

Distribution Assumptions in Ridge & Lasso Regression Models?

What are the assumptions for the distribution of the features for regression models like Lasso regression or Ridge regression? Why is it better to have features with Gaussian distributions?
1
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1answer
143 views

Intuition behind RKHS

Why has RKHS become such an important concept in machine learning in recent times. Is it because it allows us to represent a function of combination of linear functions? What areas of mathematic does ...
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0answers
63 views

Adaptive Learning Rate Convolutional RBM?

I was wondering if anyone was aware of some work done for Adaptative learning rate for Convolutional RBM training ? KyungHyun Cho published an algorithm for RBM (Enhanced Gradient and Adaptive ...
1
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1answer
33 views

Expectation propagation for feature selection

I'm using Expectation propagation algorithm (infer.net library) for my feature selection problem. I generate input data and test my model. The thing is that when ...
4
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2answers
202 views

Growing number of Gaussians in a mixture

Let I have a Gaussian mixture consisting of $n$ Gaussians that is already fitted (e.g. using EM algorithm) with respect to a given data set. Now I want to add one more Gaussian to make the mixture ...
0
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0answers
50 views

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 ...
4
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1answer
148 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 ...