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

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Gaussian classifier: if two gaussians have equal variance is it possible for them to produce a non-linear decision boundary?

I have been playing with this a bit and I don't believe they can. However, I am very new to machine learning and my maths isn't strong enough to be certain.
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13 views

Is this feature redundant?

Say I have a data set, and there's one feature that divides the set into roughly two halves, labeling one half A, and the other half B. Now I have another feature, it labels all instances that were ...
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25 views

Neural Network System Identification

I am trying to implement a Neural Network to identify a Nonlinear System. I have implemented a very simple system in simulink and on the basis of examples of its input and output I would like to have ...
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15 views

Gaussian Mixture and K-Means ?! a big challenge?

This is taken from Tom. Mitche Material as Old-Exam. I think the (2) is true and not (3). Who can verify me?
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5 views

Train model based on correlations

I have a dataset of all trains in my country for a period in time, in a MySQL database. The form of this data is the following: ...
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16 views

Decision Tree in test, a Wrong Problems? [on hold]

I took a test two days ago. one of our question is as follows: decision tree with depth 2 is constructed for two binary feature. hypothesis spase that can be shown with the following tree has ...
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31 views

Does Dynamic Bayesian Network have to be symmetric?

I want to create a Dynamic Bayesian Network with 2 time slices, each with 12 nodes. This is the network I made: Some people in my research group said that this is not DBN because its inter-slice ...
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10 views

N-gram learning vs stochastic learning

I'm interested in comparing the differences in learning in n-grams and gradient-based learning (in my case with neural networks), particularly in the context of language modelling with the two classes ...
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14 views

K-cross validation and Naive Bayes

I am doing an exercise of machine learning, and I have built a Gaussian Naive Bayes classifier (i.e., I have defined values of mean and standard deviation) using scikit-learn. Now I am supposed to ...
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36 views

Neural network & Bayesian in this machine learning algorithm

I am new to machine learning etc and found this comprehensive algorithm: http://scikit-learn.org/stable/tutorial/machine_learning_map/ . However, I am not able to make out any reference to neural ...
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19 views

An example for a finite hypothesis class which is not PAC learnable?

Finite hypothesis class with bounded loss function are PAC learnable. Are there examples for finite hypothesis classes in the case of unbounded loss function, which aren't PAC learnable?
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41 views

R:Text Analysis and Classify as type

I am new to R and Analysis, I have content set (emails) that are stored as csv file that is of more than 1000 rows(more than one email content in a row) , these are been imported to R and have been ...
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10 views

R: Caret Package - Regression with (strong) confidence?

I am fairly new to using prebuilt machine learning packages in R. I am looking at the following problem. I have a long feature set, very small training set, and a large test set. The goal is to ...
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6 views

Denoising Autoencoders weights at test time

When using masking noise on Denoising Autoencoders,Should weights be increased at test time proportional to the masking rate as in Dropout?
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19 views

PAC learning model definition

The probably approximately correct (PAC) learning model definition is: A concept class $C$ is said to be PAC-learnable if there exists an algorithm $A$ and a polynomial function $poly(·,·,·,·)$ such ...
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33 views

Support Vector Machines vs KNN

It was my understanding that in a separable case, SVMs produce the best separation possible and therefore will always produce the same or a better classification rate compared with say, 1NN, ...
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6 views

Updating model parameters online on test data

I learn the parameters of a temporal model (in my case, an RTRBM) on some training sequences using mini-batch gradient descent. Let's say now that I am updating my model online after every prediction ...
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15 views

implementation of Poisson regression [duplicate]

I am trying to work with Poisson regression. I came across this video which is very helpful - https://www.youtube.com/watch?v=HntUY8SsYZg. In the video one of parameters (Race) is categorical and ...
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13 views

embed histogram into larger feature vector

in my problem I have feature sets extracted by different methods and I want to put them into one single feature vector. However, one of these feature sets is a histogram which I don't know what's the ...
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11 views

What is inference, training and testing in Undirected Graphical Model?

I have a Undirected Graphical Model (UGM) - $ \sum_i w_i\phi_i $ . What is the inference and training here? Suppose I have a train data and test data how do I train and test using this data and UGM? ...
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15 views

predict for rpart model [on hold]

I do cross validation for doing rpart model, exactly I do leave one out(LOO)(one row fro testing teh model and the others for learning teh model) so the testing set will consist of one row for each ...
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12 views

How to determine number of feature maps in the convolution layer of a CNN?

How do we know how many feature maps is needed in the convolution layer? Other steps is clear to me except that convolution steps.
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16 views

hinge loss vs logistic loss advantages and disadvantages/limitations

Hinge loss can be defined using $\text{max}(0, 1-y_i\mathbf{w}^T\mathbf{x}_i)$ and the log loss can be defined as $\text{log}(1 + \exp(-y_i\mathbf{w}^T\mathbf{x}_i))$ I have the following questions: ...
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28 views

SVM output to probabilistic affiliation

How can I convert the svm output for multiple class classification(one vs one approach) to probabilistic values? Meaning that I want to have a probability for a tested element to be in each available ...
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33 views

Nearest algorithm according to which the humans analyze the data [closed]

Which Algorithm analyze the data just like the people does? Nearest algorithm according to which the humans analyze the data Can I say that the people group the data similar to the s.link algorithm ...
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1answer
25 views

A question about SVM kernels

this is a very basic question about SVM. I was using SVMs that are provided in the scikit for some problems, and noted that they are quite slow for big datasets. I then learned more about the ...
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2answers
53 views

Using Neural Net weights as input to another classifer

Is there anyway to use the weights from a neural net hidden layer as input to another classifier, say a random forest? Of course this is trivial for the training data but how to score new data? Are ...
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19 views

General use of Relevance Vector Machines [closed]

I am not too versed in patent law and thus I am turning to you guys. I have been very interested in trying to implement Relevance vector machines (RVM) in python, however while researching them, I ...
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10 views

Number of valid weight combinations in an ANN

Is it correct to assume that there is an infinite number of combinations of weights that a neural network can have in its connections in order to produce a specific output when given a certain set of ...
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1answer
48 views

Effect of categorical interaction terms with random forest machine learning algorithm

Thanks in advance for the help. I have moderately large dataset (around 7000 samples) with numerous categorical predictors and a single binary response. All of the predictors are categorical. ...
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16 views

What are the benefits of using ReLU over softplus as activation functions?

It is often mentioned that rectified linear units (ReLU) have superseded softplus units because they are linear and faster to compute. Does softplus it still have the advantage of inducing sparsity or ...
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22 views

How Linear SVM works for Text Classification

I am working on text classification problem with Linear SVM. I have some basic knowledge on SVM. I am looking for information on how exactly SVM works for text classification problems, i.e. its ...
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6 views

How can be assesed that a given data representation is better than the other?

Given a classification dataset, suppose I learn many different data representation with Matrix Factorization, Clustering or with such approaches. At the end , how would I decide which is better than ...
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17 views

Estimating class probabilities given discriminative functions per class

What is the effective way to estimating class probabilities per class, if I know discriminative functions for each class (I have trained ML models giving some scores). My naive implementation is to ...
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6 views

Similarity of noisy and non-noisy learned concepts when target concept not in concept class?

We know that, given a set of examples $X$ with one-sided noise (with noise parameter $\eta$), that using minimum one-sided disagreement, we can PAC-learn a concept class $\mathcal{F}$; that is, with ...
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1answer
36 views

How can Markov cluster algorithms be used to cluster strings?

I have just start learning about Machine Learning and while surfing on the web, I saw that another CV user in those post has offered Markov cluster algorithms to cluster long strings. As far as I ...
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2answers
74 views

Machine Learning for Image Processing book recommendation

I'm searching a good (and compact) book about multivariate pattern analysis in images with machine learning techniques. I took a machine learning course and used for it the Bishop book but I found it ...
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1answer
23 views

For a quadratic form to minimize with a L2 regularization term, is the gradient of the solution collinear to the solution?

Say you minimize a quadratic form f with a L2 regularization term (g = f + L2_term). The solution of minimizing g is x*. Is the gradient of f applied to x* collinear to x* as the figure below ...
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28 views

Boosting Explained

I'm a newbie trying to learn Boosting. The examples I found online are quite confusing. Is there a simple tutorial somewhere that explains ...
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21 views

How to draw plot of the values of decision function of multi class svm versus another arbitrary values?

I am trying to draw a plot of the decision function ($f(x)=sign(wx+b)$ which can be obtain by fit$decision.values in R using the svm function of e1071 package) versus another arbitrary values. From ...
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1answer
18 views

Assessing model performance of stochastic algorithm

I'm looking at how I currently evaluate my classification models and wondering if it could be improved. I've got a stochastic algorithm (Genetic Programming), which for non-classification problems is ...
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1answer
54 views

Neural networks: how can convex optimization produce different weights each time?

I am training a multilayer perceptron with a logistic activation function by backpropagation. The weights are not unique - each time I redo the fit, I get a different set of weights. However the ...
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1answer
40 views

Machine learning framework for SVM, Random Forest

I need an library, or something that is already done for SVM and Random Forest algorithms. Can you give me some ideas? I don't have experience and I don't know what to choose. The restriction of my ...
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20 views

Why R linear ksvm don't use all support vectors

I was playing with svm and I made this data: x y | type ----------- 1 1 | 1 2 2 | 1 2 0 | 1 0 0 | -1 0 1 | -1 1 0 | -1 and using this setting ...
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20 views

How to build separate time series forecasts model for each of 3k customers?

I have 3000 customers in my base and i want to forecast next 6 months revenue for each of these 3000 customers. Does that mean i have to build 3000 arima models 1 for each customer? I can build a ...
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25 views

Do you have any information about regression model and logistic regression notes [closed]

Please share the link or any source for statistics notes to refer for in-depth study.PDF is also welcome.I want specially Regression Model,Machine Learning & Logistic Regression notes.
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2answers
55 views

How To Better Represent A Problem To A Machine Learning Algorithm

I am familiar with the basics of how to present a problem to a machine learning algorithm using binary encodings. I am also familiar with, but still learning about, feature selection/extraction and ...
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1answer
63 views

Advantages of taking the logarithm to minimize the likelihood

In regression/classification problem, we are often interested in minimizing a cost function with respect to the parameters of the model. In many cases, the cost function is the negative likelihood. To ...
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13 views

R package for generating group members suggestions [closed]

I have a list of groups, and each group has a different number of entities assigned to it, i.e.: ...
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1answer
9 views

Calculation of Cosine Similarity using LSA

I am getting negative cosine similarity value between two documents in Latent Semantic analysis. How should it be treated?