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

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2
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1answer
85 views

R: Finding relationships between 2 variables to determine any patterns in data

I am working on finding relationships/patterns between 2 variables (Type_A, Type_B). ...
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1answer
29 views

Generative algorithms

If I understand slide 4 correctly, the idea here is that in order to compute $p(y|x)$ we can use the fact that $$p(y|x) = \frac{p(x|y)p(y)}{p(x)}.$$ Then $p(x|y)$ and $p(y)$ are calculated using our ...
5
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1answer
93 views

Do CART trees capture interactions among predictors?

This paper claims that in CART, because a binary split is performed on a single covariate at each step, all splits are orthogonal and therefore interactions among covariates are not considered. ...
3
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2answers
79 views

Are Random Forests and Boosting parametric or non-parametric?

From this excellent paper by Breiman, we can seize all the difference between traditional statistical models (e.g., linear regression) and machine learning algorithms (e.g., Bagging, Random Forests, ...
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0answers
11 views

Variable representations for faster learning convergence

My notes on machine learning state that transforming a classification problem from 2 classes, class A = 0, and class B = 1, to class A = $(1,0)$, and class B = $(0,1)$ leads to faster convergence in ...
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0answers
18 views

How could these two simple Bayesian algorithms be explained, simply? [closed]

count(this token in class) + 1 / count(all tokens in class) + count( all tokens ) and ...
2
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0answers
36 views

How to make use of less data of a particular class for better modeling?

I have a dataset, say 9000 rows, with some features. Around 8000 belong to class 1 and 1000 to class 0. So, if I am creating a model with any method say SVM, LR, Random forest the model has a tendency ...
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0answers
64 views

In machine learning, may I train correctly a neural network with input real data and output validation Boolean data?

I have a matrix made of ~ 100 rows and 12 columns. Each entry contains a real value. The first 6 columns refer to a particular concept (firstClass), the following 6 to another one (secondClass), and ...
1
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1answer
70 views

What is the honesty condition for regression trees?

I have a question pertaining to Stefan Wager's "Asymptotic Theory for Random Forests": http://arxiv.org/pdf/1405.0352v1.pdf Wager first states that trees are "fully grown in the sense given training ...
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0answers
15 views

RBM hidden units becoming correlated

I am trying to train an RBM with 8 hidden binary units and 40 visible ReLUs. At first, I had issues with binary units becoming stuck due to the weight saturating, but I got rid of that problem by ...
-1
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1answer
35 views

What are the benefits for semi-supervised learning over unsupervised clustering? Or any limitations?

I have another question about semi-supervised learning vs unsupervised clustering, what are the benefits and limitations? I have got some data with labels and some without labels. I performed ...
1
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0answers
27 views

Semi-supervised learning vs supervised learning, what are the benefits and limitations?

Just wondering if any previous work compared semi-supervised learning vs supervised learning? Currently, I have got both datasets with and without labeling. And therefore, it is intuitive for me to ...
0
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1answer
22 views

Determine confidence in a CART model with factor (2 levels) response variable (using rpart)

I use the package rpartto model a classification/regression tree. I have the variables $x,y,s$ where $x$ is in $\{-1,1\}$, y is continuous in $[0,1]$ and $s$ is a ...
2
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0answers
31 views

Reinforcement Learning in Industry [closed]

This is my first post here I would like to start with a rather general topic of discussion. I have studied Reinforcement Learning during the university years and although I find it rather fascinating ...
1
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1answer
32 views

Expectation of squared error

In machine learning, we let $X$ be a real-valued input vector and $Y$ be a real number output, with joint distribution $P(X,Y)$. We are looking for a function $f(X)$ for predicting $Y$ given the ...
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0answers
27 views
0
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1answer
28 views

Online gradient descent for strongly convex function

Given that our loss function is $\alpha$ strongly convex function which means $\mbox(\nabla f(\mathbf{x})-\nabla f(\mathbf{y}))^{T}(\mathbf{x}-\mathbf{y})\geq \alpha||\mathbf{x}-\mathbf{y}||_{2}^{2} ...
3
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1answer
43 views

How is the confusion matrix reported from K-fold cross-validation?

Suppose I do K-fold cross-validation with K=10 folds. There will be one confusion matrix for each fold. When reporting the results, should I calculate what is the average confusion matrix, or just sum ...
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0answers
13 views

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.
0
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1answer
33 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 ...
1
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1answer
38 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 ...
2
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0answers
96 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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0answers
34 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 ...
0
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0answers
17 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 ...
0
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0answers
19 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 ...
0
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1answer
49 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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0answers
32 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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0answers
52 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 ...
0
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0answers
31 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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0answers
13 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?
0
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0answers
23 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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0answers
63 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, ...
0
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0answers
8 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 ...
0
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0answers
16 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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0answers
22 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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0answers
24 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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0answers
34 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 ...
1
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1answer
35 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 ...
2
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2answers
55 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 ...
2
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0answers
31 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 ...
1
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0answers
11 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 ...
3
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1answer
75 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. ...
0
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0answers
47 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 ...
1
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1answer
43 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 ...
0
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0answers
7 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 ...
0
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0answers
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 ...
0
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0answers
8 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 ...
0
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1answer
52 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 ...
2
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2answers
87 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 ...
0
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1answer
35 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 ...