Tagged Questions

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

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10 views

How to estimate the correlated individual components from a sum, for a random process?

Assume that there are $N$ realisations of five individual, random variables$X_1$, $X_2$, $X_3$, $X_4$ and $X_5$, which in general could be correlated. We define another random variable ...
2
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0answers
3 views

Understanding sample complexity in the context of uniform convergence

I was reading Andrew Ng's notes and on page 6 he mentions (uniform convergence): $$Pr[\forall h \in \mathcal{H}_{finite}|\epsilon(h_i)-\hat{\epsilon}(h_j)| \leq \gamma] \geq 1-2ke^{-2\gamma^2m}$$ ...
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0answers
7 views

In Hidden Markov Model (HMM), is the transition matrix known, inferred, or assumed?

I'm reading Kevin Murphy's Probabilistic Machine Learning, which explains the forward algorithm to do filtering in HMM as follows (pp 610): The very first line says that the transition matrices ...
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1answer
16 views

Number of variables for decision trees

I have a data with just 5 independent variables and a response. I am dealing with a classification problem. Will decision trees perform well or the number of variables have to be higher to get ...
1
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1answer
33 views

Combining pca and classification algorithms

For some classification algorithms, assuming independence of data helps reduce the number of parameters to estimate. Why then not just to apply a method like pca or ica to the original features to get ...
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1answer
15 views

Machine learning : learn feature value range for a classification

Which domain the problem belongs to? Given a set of products some are classified as cheap and some not. The task is to determine the price range (probablistic) for cheap products ? Supervised ...
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1answer
26 views

Machine Learning for Text Classification

I am new to Machine Learning.I am working on a project where the machine learning concept need to be applied. Problem Statement: I have large number(say 3000)key words.These need to be classified ...
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0answers
18 views

Unsupervised learning algorithems to detect anomaly in waves

I have a sample of graphs (more then 10000...). that look like in the image below: I am searching an Unsupervised learning algorithems thet can help me to detect Anomaly observations. Here what i ...
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0answers
14 views

How can one go about recognizing a kind of motion using 3D depth data?

I'm using a Kinect device, and I'm currently extracting Joints, and Depth data unto probably a buffer data of 15 frames. This is done at 30 frames per second. The whole point of it is to try and ...
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0answers
20 views

Making a neural network model more sensitive to one of its several inputs

I am currently using neural network methods in R to model energy consumption (response) based on temperatures, previous consumption values and weekend dummy variables (inputs). Unfortunately, the ...
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0answers
24 views

SVM Dual Formulation :: KKT Constraint

In Andew Ng's SVM course notes, the final hard margin optimization problem is given as the following: I am unclear how to see from this where the 5th constraint is satisfied. The definition of ...
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0answers
10 views

Support vector regression in weka

I am using SVR for statistical down-scaling of precipitation. I have taken the first 3 factor scores in principal component analysis of variables as predictors and precipitation as predictand. As ...
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0answers
24 views

In natural language processing (NLP), how do you make an efficient dimension reduction?

In NLP, it's always the case that the dimension of the features are very huge. For example, for one project at hand, the dimension of features is almost 20 thousands (p = 20,000), and each feature is ...
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0answers
25 views

Gradients of marginal likelihood of Gaussian Process with squared exponential covariance, for learning hyper-parameters

The derivation of gradient of the marginal likelihood is given in this pdf, equation 5.9. But the gradient for the most commonly used covariance function, squared exponential covariance, is not ...
2
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1answer
63 views

How to estimate the individual components from a sum for a random process?

We have $N$ realisations of five individual, IID random variables $X_1$, $X_2$, $X_3$, $X_4$ and $X_5$. We define another random variable $S = X_1+X_2+X_3+X_4+X_5$. Now, for a given $S$ generated from ...
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0answers
24 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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2answers
41 views

Mining patterns in continuous sequence

I have data in form of $N$ sequences $s_j=(t_i, e_i)_{i\in\{1,\ldots,n_j\}}$ with $n_j$ data-points each, where $t_i$ is a time-stamp and $e_i$ is a categorial event, say $e_i\in\{A,B,C,D\}$. The $N$ ...
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0answers
9 views

Is it OK to resample data in one vs all multiclass classification?

I have a multiclass classification problem and decide to use one vs all logistic regression. Since some classes are very rare (pos vs neg is like 1:100), I plan to use some balancing strategy during ...
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1answer
98 views

Why is the definition of a consistent estimator the way it is? What about alternative definitions of consistency?

Quote from wikipedia: In statistics, a consistent estimator or asymptotically consistent estimator is an estimator—a rule for computing estimates of a parameter $θ^*$—having the property that ...
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0answers
28 views

Adding weights to data points in logistic regression

I am trying to run a logistic regression on a data sample where the unique identifier is "project". I also have the date on which each project was created. Some projects are more recent than others ...
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0answers
10 views

Normalisation formula applicable to one or more data items

I've created a recurrent neural network, to which normalised values are passed as inputs. The normalization formula is: $$\tilde{x_{i}} = \frac{1}{1+exp(-\frac{x_i-\bar{x}}{\sigma})},$$ where ...
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0answers
11 views

Using PyBrain after training a network

I'm using PyBrain to create a neural network. I'm still pretty new to neural networks and their concepts. I've so far only run train() over the network, as ...
1
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1answer
15 views

How to get random classification to assess the performance of classifier with McNemar test?

I'm trying to replicate a study where the author used the McNemar test to assess the performance of classification compared to random classification. I have the original classifier and I'm using R to ...
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0answers
13 views

How to categorize classifiers and matrix factorization methods?

I have a classification problem which is solved by a variety of methods. Among the methods are unsupervised methods, traditional classifiers and a supervised matrix factorization methods. The problem ...
1
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1answer
22 views

How do I incorporate the biases in my feed-forward neural network

I'm trying to implement a FFNN. I'm doing this as an excercise to understand how biases play a role in the classification. I trained a NN using a package in R with the inputs being 1..100 and the ...
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2answers
37 views

Why Adaboost with Decision Trees?

I've been reading a bit on boosting algorithms for classification tasks and Adaboost in particular. I understand that the purpose of Adaboost is to take several "weak learners" and, through a set of ...
0
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1answer
52 views

Gaussian is conjugate of Gaussian?

Someone told me that, Gaussian distribution is conjugate to distribution because a Gaussian times a Gaussian would still be Gaussian distribution ? Why is that ? Say the following situation: $X\sim ...
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0answers
13 views

Predicting the near-future values using an unevenly sampled time-series data

Summary Need help with predicting the near-future values using an unevenly sampled time-series data. Data is collected as events, and is converted to time series. I have tried out a few approached ...
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0answers
15 views

Why all coeficents of features of model are zero while I have high deviance using glmnet?

I'm using gmlnet to learn lasso regression model. model<-cv.glmnet(x, y, alpha=1, nfolds=10,parallel= TRUE) when I learn model and look at the model it's like this : ...
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0answers
22 views

Machine learning and Partial differential equations

Are there any algorithms which were developed using partial differential equations for tackling some of the machine learning problems? Most works I see online are in the field of computer vision and a ...
0
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0answers
13 views

data amplification

I have obtained few data points from my client, and want to increase the number of data points keeping the complexity of the original almost same. and in the second part increasing the data ...
1
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0answers
24 views

A measure of correspondence between ranked ordinal data

I would like to find an appropriate way to measure the similarity between two sets of data with the following characteristics: Both sets contain 10 categorical observations. The categories ...
1
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1answer
40 views

How to handle missing data in a small $n$ large $k$ machine learning scenario?

I have a sample size $N=130$ and $1000$ variables. I am using machine learning techniques (SVM) for analysing the data. Some variables in the dataset have values that are so huge that they must be ...
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0answers
8 views

How process mining compares to probabilistic model (PGM)?

process mining can discovery graph model of process, so can probabilistic-graphical-model (...
0
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0answers
22 views

R, Confusion Matrix in percent [migrated]

In R how to get Confusion Matrix in percent (or fraction of 1). The "caret" package provides useful function but shows absolute number of samples. ...
0
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0answers
9 views

Standard error of prediction MARS splines earth package

I'm using the earth package (using caret train function) MARS spline implementation in order to perform non - linear regression modeling. I would like to obtain a measure of prediction uncertainty ...
1
vote
1answer
42 views

Ratio between positive and negative examples in a training problem

When training a 0/1 classifier, what should be the ratio of positive to negative, how to decide the ratio between them based on the classifier I use and the data set under analysis?
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0answers
7 views

Grouping team features and comparing them to a single match outcome

I have two teams, and both teams have multiple features related to that team. For example: Players in the team. Players total points won. Team win percentage. Average player weight. Average player ...
0
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0answers
11 views

Is the approach for PLSDA for categorical variables the same as that used for “PLS for regression”?

I understand the approach used for partial least squares for regression (PLS) where the principal components are chosen such that the correlation between the scores in the principal component space ...
0
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0answers
31 views

What's an intuitive explanation of how the Otsu's method works? [closed]

i would use otsu's method for reduction of a gray level image to a binary image but i don't understand how it works,i need your help
0
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1answer
15 views

Label propagation in semi-supervised learning

Suppose we have a set of labeled and unlabeled instances. 70%unlabeled 30% labeled. We apply a semi-supervised algorithm. Let's say we apply S3VM or Laplacian SVM. We use all the data available. When ...
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0answers
21 views

Machine Learning books for CS (non-statistician) grad student [duplicate]

What books on machine learning are recommended for a CS graduate student without a huge background in statistics? I do have some background in ML (and of course linear algebra, probability, etc.) but ...
1
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1answer
55 views

Assume (x,y) are drawn from independent & identical distribution when y=f(x)

Sometimes we say the following: $X$ is some training data given by $X:=\{(x_1,y_1),...,(x_l,y_l)\}\subset R^d \text{x}R$. Assume that the training data had been drawn from independent and identical ...
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0answers
33 views

Learning Decision Trees on Test Data Using R

How can I use R to learn classes on test data? I currently have a training set of about 1000 entries and a test set of about 10000 entries. I split it up so that the training set has the class label ...
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0answers
14 views

Optimal Margin Classifer : Optimization Problem Setup

In the notes from Andrew Ng Machine Learning course, he writes the initial optimization problem as follows. I am confused by the notation and suspect I am missing something simple. Given the ...
0
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0answers
24 views

Greedy subtree selection in Nested Hierarchical Dirichlet Processes

I'm implementing the Nested Hierarchical Dirichlet Process as described in this paper by Paisly et. al, 2014: http://arxiv.org/abs/1210.6738 My question is about the variational objective in Equation ...
0
votes
1answer
14 views

What is the intuition behind the Kappa statistical value in classification

I understand the formula behind the Kappa statistic value and how to calculate the O and E value from a confusion matrix. My question is what is the intuition behind this measure? Why does it work so ...
0
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1answer
17 views

How to choose an appropriate maxdepth in rpart.conrol?

I'm using the boosting method in adabag library and trying to choose an appropriate maxdepth in rpart.control for building a 2-class classification model using my training dataset. I have noticed that ...
0
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0answers
12 views

Can you use accelerometer data for classification with Conditional Random Fields?

I want to recognize activities, based on accelerometer data from the smartphone. I studied Conditional Random Fields and the CRFSuite. Now I am Confused. In my opinion CRF training uses static single ...
0
votes
1answer
29 views

Vector Space Model for Online News Clustering

I am trying to automatically cluster news articles based on their content. I need this algorithm to be online and simply group news articles related to the same story as they arrive. The common ...