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

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36 views

Finding best weights for ranking

I have a problem concerning Data Science and Machine Learning, and maybe somebody could share a hint on how to accomplish or where to begin with. Thanks in advance. The thing is I have an application ...
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24 views

How does batch normalization compute the population statistics after training?

I was reading the batch normalization (BN) paper (1) and it said: For this, once the network has been trained, we use the normalization $$\hat{x} = \frac{x - E[x]}{ \sqrt{Var[x] + \epsilon}}$$ ...
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45 views

How and why does Batch Normalization use moving averages to track the accuracy of the model as it trains?

I was reading the batch normalization (BN) paper (1) and didn't understand the need to use moving averages to track the accuracy of the model and even if I accepted that it was the right thing to do, ...
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12 views

Reference request about feature maps in ML

Can someone kindly link to some recent papers on understanding feature maps in ML? It would help to get an idea of what are the recent issues there that people have been working on with regards to ...
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22 views

From MDP to SMDP: What is it in a nutshell

Markov Decision Process (MDP) is a mathematical formulation of decision making. An agent is the decision maker. In the reinforcement learning framework, he is the learner or the decision maker. We ...
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46 views

Free space detection on image

I have set of images with front or back rear cars which was obtained by cascade detector. I need to detect free space on image, (more precisely I need just to know how big car is in pixels). ...
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1answer
41 views

Learning Algorithm used in H2o Deep Learning [closed]

I have started to learn H2O deep Learning which is DNN(Feed Forward Neural Network ),I have read the documentation to get me started but i struggle to understand clearly, i come up with some confused ...
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14 views

Doll Detection - looking for deep learning net or real time able detector. [closed]

I want to detect some dolls [1] (as victims in a catastrophic scenario!). My problem is that I tried it with the HaarCascades Classifier and the CNN Deep Learning Network. HaarCascades is ...
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29 views

Plot a subtree from a big decision tree

I am working on my thesis using decision trees. I am presenting the resulting tree to show how they help in exploring data. My issue is that since the tree is big, I want to break it down into parts, ...
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5answers
982 views

Overfitting: No silver bullet?

My understanding is that even when following proper cross validation and model selection procedures, overfitting will happen if one searches for a model hard enough, unless one imposes restrictions on ...
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1answer
63 views

How can I experiment with lagrange multiplier in PCA optimization

Suppose we want to solve following optimization problem (it is a PCA problem in this post) $$ \underset{\mathbf w}{\text{maximize}}~~ \mathbf w^\top \mathbf{Cw} \\ \text{s.t.}~~~~~~ \|\mathbf w\|_2=1 ...
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23 views

Perfect recall and spurious states in hopfield networks

In Hopfield networks, one can apparently load perfect recall into the network (by having enough neurons compared to patterns). (Source) However, at the same time, it appears that spurious states (i.e....
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10 views

I want to do times clustering with dtw but don't konw how [closed]

I have a piece of new data of 2 days with one dimension ,i want to search the most similar data in the history like 10 years whit dynamic time wrapping , how can i do this with r? I find a package ...
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2answers
22 views

The importance of lambda in a regularization function, with respect to the hypothesis.

I'm working through some parts of Russel & Norvig's Artificial Intelligence, and this is the cost function they give: Cost(h) = EmpericalLoss(h) + λComplexity(h) How does choosing a value for ...
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0answers
25 views

Re-estimation of Hidden Markov Model Parameters without Knowing Hidden States

I have been working on Hidden Markov Models (HMM) for a while. I thought that I understood the basics of HMM, however, recently I have confused about a point. Here is the issue: Recall the 3rd ...
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30 views

Advanced and most recent feature extraction technique

what are the most advanced feature extraction technique for the medical image ? I meant much advanced than wavelet transform, ripplet transform , dual-complex wavelet transform , curvelet transform &...
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1answer
105 views

How to prove this Gaussian Mixture theorem? (Fitting/Overfitting)

In certain applications, we approximate an unknown pdf by placing uniformly weighted Gaussian terms at each of some sample points $\{x_{1},...,x_{n}\} $ and assigning some variances $\{\sigma_{1},...,...
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13 views

What does mean by the number of pixel positions in CNN

I am doing project in machine learning using deep CNN. I need to understand how to choose hyperparameters (number of filter, shape of filter, max pooling shaep,..). I am using image of 42*42 (...
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1answer
48 views

How to create a regression model if data points are structure as seen in the graph?

This is a graph of revenues for different products with the Y-axis showing normalized revenues (mean of 3 and SD of 1) and X-axis is weeks. I need do a regression analysis of sorts on this data and am ...
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19 views

What preprocessing techniques work well for autoencoding audio?

I am wondering what preprocessing techniques work well for autoencoding audio data? Specifically I have a dataset of ~0.5 second audio samples of people pronouncing digits 0-9 (think an audio version ...
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14 views

About derivation of “regression minimizes empirical risk”

I've been reading Vapnik's Statistical Learning Theory. In his book, regression is defined as $r(x) = \int ydF(y|x)$, where $F(y|x)$ is conditional (cumulative) probability distribution. The risk ...
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2answers
30 views

How do you know that the assumptions of the model have been satisfied, and it’s ok to run the algorithm? [closed]

when using any simple algorithm like logistic regression, svm or even complex ones, how could you know that it’s ok to run the algorithm and use it in the industry? for example :when using logistic ...
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3answers
24 views

Principal component analysis result

I'm trying to understand the result of PCA, thought you can help me to understand better. ...
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1answer
126 views

Gradient for logistic loss function

I would ask a question related to this one. I found an example of writing custom loss function for xgboost here: ...
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1answer
31 views

Feedforward Vs. Backpropagation Neural Network

I was taking this course Machine Learning-Coursera (Standford) by Andrew Ng In Week 4 and Week 5 we have given ...
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0answers
19 views

neural networks: how to deal with related inputs?

I am a neural network newbie, so please don't blame me if the question is silly. How to handle related inputs in a neural network? Let's explain it with an example: Suppose my NN expects a vector of ...
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1answer
32 views

What is the proper name for “unknown data” set in machine learning?

As far as I know in practice the whole training set is usually split into training, validation and test[1] sets. Training set is used to train the model, validation to tune the parameters and test set ...
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14 views

Non-parametric smoothing a small sample time series with fixed/known t(0) and t(n)

I would be grateful for any suggestions on how to smooth a time series with the following properties: We observe $t(i)$ for every integer $i=0...T$. $0 < t(i) < \infty$ $T$ is typically small (...
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1answer
67 views

Find out if using k-fold cross-validation helped to overcome overfitting (Machine Learning standard)

One of the main way to overcome overfitting is using $K$ fold cross-validation, and as this paragraph in cross-validation wiki page says: The goal of cross-validation is to estimate the expected ...
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43 views

Calculating conditional probability in Bernoulli mixture model

I'm following the book Pattern recognition and machine learning by Bishop on Bernoulli mixture model, and trying to code it. But I don't understand how to calculate the conditional probability (page ...
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1answer
25 views

Neural Networks with Input 0

Imagine you have a Neural Network with Sigmoids. It has an input $x$ and so a node would output $\tanh x$ to a connection. The connection would then output $w*tanhx$ where $w$ is the weight of the ...
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4 views

How much variance is captured by the RFF maps?

The RFF maps here are possibly the most used feature maps. I was wondering if there are cases where anyone has theoretically estimated the total variance captured by these maps? Is any simplification ...
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15 views

is the time it takes for a network to perform will on a classification problem proportional to its learning rate?

I've followed a brief tutorial found here: http://outlace.com/Beginner-Tutorial-Theano/ I wanted to know what happens when I change the learning rate. At the rate it is set in the tutorial, ...
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1answer
26 views

Is it possible to add up the accuracy rates of 2 predictors?

Weather channel 1 has a 65% accuracy rate of predicting tomorrows weather Weather channel 2 has a 59% accuracy rate of predicting tomorrows weather Is it somehow possible to take into account ...
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1answer
18 views

Is the EM-algorithm the same thing that variational inference in LDA?

I am new in the probabilistic topic modeling, and I need to understand deeply the LDA process, I understand what want to do the inference process in LDA, and I understand too that there is 2 "types" ...
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1answer
78 views

TOO low estimated SVM probability for most of the negative test examples?

I am using LIBSVM (as well as the fitcsvm and fitSVMPosterior of ...
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17 views

Deep Belief Network configuration for dice face recognition

I should develop a network that can read the result of throwing a dice. I have a dataset which consists on a synthetic collection of such images, together with the corresponding target values. Each ...
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27 views

how to understand 'end to end' in deep learning?

Recently, I do some literature research about CNN and find there is a concept of end to end training Such as the abstract in Fully Convolutional Networks for Semantic Segmentation How to ...
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28 views

Is it ok to use symmetric loss function when evaluation metric is asymmetric?

I completely understand that it's ok to use a loss function different from the evaluation metric. For example, accuracy isn't computationally feasible to optimize directly since it's not ...
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1answer
10 views

False positive rate of Bloom Filter

A Bloom Filter is used to identify if an element $x$ in a data stream is a member of some set $S$. What's the false positive rate of a Bloom filter with a single hash function $h(.)$?
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21 views

How to convert standardized line graphs into a function?

I have graphed revenues from 4 different products of the same variety (ie. four different movies and the revenues they brought in). The axes are: x is the weeks and y is the revenues; with the lines ...
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22 views

Reverse-Engineer Time Series Matrix With Machine Learning

I'm trying to figure out the following situation which is almost the same as in this post here Time series with multiple subjects and multiple variables ...
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3answers
32 views

pattern recognition for sequence data

There are a lot of data sequences, I am trying to find pairs of sequences that are similar with other. Trivially, we can define some distance measure, and compare each pair of sequence in terms of ...
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41 views

Recursive feature elimination and class imbalance

I am trying to apply the recursive feature elimination in the R package caret following the example in the caret website: ...
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30 views

Classification technique for unsupervised data?

I have unsupervised data it is a mix of continuous and categorical data. Now I want to classify the test data into three categories on basis of my unsupervised data. The approach I took is first do ...
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20 views

What does M in AdaBoost.M1 and AdaBoost.M2 stand for?

It is obvious that AdaBoost stands for Adaptive Boosting, but what does the M1 and M2 signify? I am not able to find its linguistic significance other than its description and algorithmic profile.
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9 views

what 's the difference between GBM and BRT [duplicate]

Gradient Boosting Machine (GBM) Boosted Regression tree (BRT) what's the difference? It seems the same.
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34 views

Adaptive baseline for time series data for anomaly detection

I would like to create/calculate a dynamic baseline for continues time-series data. The data is arriving in real-time (streaming) every N minutes interval. I googled around and came across control-...
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12 views

Fractional Model with Instrumental Variable in panel data

I faced econometric problem in my estimation strategy. My explanatory variable is a proportion (i.e. the percentage of internal finance in working and fixed capital finance) and at the same time, it ...