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

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Dummy Variables and Learning algorithms

Suppose a predictor variable $x$ is nominal/categorical with three levels: $1,2,3$. Thus we create two dummy variables $x_2$ and $x_3$ with level $1$ as the reference variable. Let $y$ be a binary ...
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419 views

Machine Learning Process for detecting edges of overlapping objects with OpenCV

I'm quite new to machine learning and a bit unsure about the whole process and the interpretation of the results. The Task: I have images with some objects of somewhat the same color and shape which ...
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2k views

full batch vs online learning vs mini batch

This is a question from a coursera course: Suppose we have a set of examples and Brian comes in and duplicates every example, then randomly reorders the examples. We now have twice as many examples, ...
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523 views

Predict observation using Hidden Markov Models

I have a sequence of observations e.g. ["Click","Scroll","Hover","Zoom","Select"]. I need to predict the next value of this observation sequence but not the next hidden state. I know that there are ...
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77 views

Conceptual question on optimization

What is the intuition and the physical meaning of the mathematical expression in convex optimization? When using optimization algorithms like particle swarm or genetic algorithm, do they have ...
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560 views

What is being learnt?

This may seem a trivial question but I have a fundamental problem in understanding learning. In supervised learning, given and input-output pair, what are we learning? Are we learning the inputs ...
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66 views

Difference between estimation and learning

What is the difference between parameter estimation which includes system identification and learning in machine learning perspective? Let say the model is y= Ax. x is the input and y is the output. ...
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195 views

The variance-covariance matrix of the least squares parameter estimation

I'm learning Linear Regression for Regression from "The Elements of Statistical Learning". Why The variance-covariance matrix of the least squares parameter estimates is easily derived from (3.6) ...
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64 views

Anomaly detection of web browsing sequences

Please consider that I'm quite new to machine learning. I need to create models based on browsing patterns of web users and find deviations from that model. I'm using web server access log files. For ...
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158 views

Basic idea of zero inflated two part models(hurdel) and application to big data (machine learning)

I'm currently working on the data which has 90% 0s in response variable. Based on my research, it seems zero inflated models could be a solution to this. However, while I was reading related ...
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141 views

How to understand kernel functions and how to choose a suitable kernel?

I am trying to describe my understand of kernels in the Support Vector Machine(SVM) and why some of them are more popular, but I am not sure if I misunderstand these concepts: 1) There are a large ...
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1answer
77 views

MDP value iteration

In Markov decision processes, what is the guarantee that value iteration chooses the same policy action from a given state for every iteration? I am referring to the slides given by AWM at ...
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435 views

ROC vs. Accuracy [duplicate]

If you want to compare two learning algorithms, which metric is better to use in general: ROC or accuracy? I understand that in ROC, you get both the sensitivity and specificity?
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28 views

Abnormalities in results L-LDA

For my research I am using Labelled Latent Dirichlet Allocation (L-LDA) on Reuters-21578 ModApte split dataset. In this dataset the news stories have a title and a body. To test the effect of L-LDA, I ...
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154 views

How do you measure the accuracy of an inference hypothesis/procedure?

Take inference to mean reasoning/predicting the value of a hidden/laten variable $Z$ given some evidence/data $X$. For example, maybe you are trying to find out if your patient has Cancer (Z = 1 if he ...
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1answer
18 views

How to apply properly k-nn algorithm when having several attributes

Let assume I have a dataset like this dataset where there are several textual attributes even continuos attributes like age. I have always encountered cases where k-nn is applied on just two ...
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2answers
3k views

About cross-validation for machine learning

Assume I have 1000 samples of data. I split the data randomly into training and test sets of size 800 and 200, respectively. Now, I train a classifier using the training set, and then evaluate the ...
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2k views

Does the vanishing gradient in RNNs present a problem?

One of the often cited issues in RNN training is the vanishing gradient problem [1,2,3,4]. However, I came across several papers by Anton Maximilian Schaefer, Steffen Udluft and Hans-Georg Zimmermann ...
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163 views

Intuition behind matrix factorization formulations?

I'm reading this paper about matrix factorization. In the paper they propose to use this factorization for the adjacency (or similarity) matrix $G$ using the following formulation: $G = U \Lambda U^T$ ...
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110 views

Assigning even partitions for Cross-Validation

This is a very basic question about cross-validation. Say that I have a sample size of 2901(or any difficult to divide number). How do I split this up into equal partitions (other than n=1)? And how ...
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76 views

One-class Classification of multidimensional vectors

I have a m x k User-feature matrix (m >> k) obtained by factorizing an original User-websites matrix (m x n) that has #page views as entries. Additionally, there are users (say r) who have been ...
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76 views

Is machine learning useful for comparing test group and control group

If you do a hypothesis to test the effectiveness of a treatment, or a marketing campaign, you want to be sure that the two groups are comparable. You can compare some relevant quantities between the ...
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86 views

Metrics for cluster evaluation

I make a set of clusters using some clustering algorithm. Precision, Recall, F Measure, Fallout and RI of individual clusters are calculated for testing the performance. How do I calculate the average ...
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231 views

Good machine learning algo for partial derivatives?

Does anyone know of good robust algos to estimate partial derivatives of a regression model? I am talking about a general regression model like this: $\mathbb{E}(y|x_1, x_2, ... x_n) = f(x_1, x_2, ...
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2k views

Training a Tic Tac Toe brain - am I on the right track?

My only experience with Machine Learning is Andrew Ng's Coursera course, but I did work through that just fine and passed with 100%. I decided to practice by making up some problems and solving them. ...
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33 views

Is Area under curve a composite function

I have some data examples. If I split the data into three parts and the have some scores for each example of the three parts and then calculate individual AUCs for the three parts In the next case, I ...
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7k views

What is the daily job routine of the machine learning scientist?

I'm a master CS student in a German university now writing my thesis. I will be done in two months I have to make the very hard decision if I should continue with a PhD or find a job in the industry. ...
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138 views

Updating SVD in Recommender Systems for change in ratings

I have read that there are projection based methods to accomodate for new user's ratings or for the ratings for a new item in SVD. However, I want to know how to update my feature space for change in ...
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38 views

how to implement linear or non linear regression for 3d position estimation?

I am a beginner in Machine Learning. For my project I need a regression algorithm that can estimate the 3D position of a device based on some constraints (moreover inputs). I know how to implement ...
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1k views

10 fold cross validation model in weka

I'm trying to build a specific neural network architecture and testing it using 10 fold cross validation of a dataset. Now building the model is a tedious job and Weka expects me to make it 10 times ...
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28 views

Number of Predictors and Classification Algorithm

In general, is it better to include more predictors in algorithms such as SVMs and random forests compared to logistic regression? It seems that when we add more predictors to logistic regression, the ...
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151 views

For a model like this what performance measures can I calculate and how?

Methods: From the machine learning literature, I understand different parameters can show performance of model in machine learning. I would briefly expand my understanding with confusion matrix: ...
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175 views

What do NORB and CIFAR stand for?

The MNIST dataset is a standard benchmark data set of digit images. MNIST stands for 'Mixed National Institute of Standards and Technology'. The NORB dataset is a commonly used dataset of binocular ...
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22 views

How to assess the importance of the features which come from intersection of features of the two models?

I have two models from two different data sets. Model 1 contain 50 features and model 2 contain 40 features. the intersection of features of model 1 and 2 is 10. so how can I assess the relative ...
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100 views

What's the optimal way to encode a 'month' feature?

What's the optimal way to encode a 'month' feature? A single integer value or 12 binary values don't quite grasp the concept of modulo distance... Say I want to train an SVM for a certain task and ...
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2k views

High precision with low recall SVM

I'm classifying a data set using SVM and those are the precision and recall values for two classes. ...
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402 views

SVM Classification with Duplicate Training Instances

I'm using SVMs with linear kernel for sentence classification (binary). My dataset contains many duplicate instances i.e. many sentences in the training set have identical feature vectors. In the ...
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25 views

How to compare the nested models which each of them comes from diffrent dataset?

I have four nested models.Every of them learned from different data sets. now I want to compare these models together.normally people try to compute the F-satistics. But for my case, it's bit harder, ...
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140 views

Difference between Factorization machines and Matrix Factorization?

I came across the term Factorization Machines in recommender systems. I know what Matrix Factorization is for recommender systems but never heard of Factorization Machines. So what's the difference?
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81 views

Expected required sample length to train a hidden Markov model

Say one wishes to train a hidden Markov model with $n$ hidden states, and (accidentally) the problem itself can be described with a hidden Markov model with $n$ (or less states). What is the expected ...
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309 views

k-means clustering on percentages

Can we do k-means clustering on percentage data (like 56%, 44%, 22%, 13%, etc.)? There is a data set, and data in various parts are measured in percentages.
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34 views

Assumption behind few latent features in recommender systems?

I know in recommender systems you have a rating matrix and then you factorize this matrix into two matrices and then learn those matrices with gradient descent. In those matrices we specify the number ...
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7k views

In Naive Bayes, why bother with Laplacian smoothing when we have unknown words in the test set?

I was reading over Naive Bayes Classification today. I read, under the heading of Parameter Estimation with add 1 smoothing: "Let $c$ refer to a class (such as Positive or Negative), and let $w$ ...
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97 views

How to decide which penalty measure to use ? any general guidelines or thumb rules out of textbook

A number of regularization measures are available in literatures, which is kind of confusing to beginners. The classical penalty is ridge by Hoerl & Kennard (1970,Technometrics 12, 55–67). ...
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376 views

How do you validate your machine learning models?

I am wondering what approaches are commonly used for validating machine learning models designed for classification or prediction tasks: Approaches that am using at the moment: Using truth-sets: - ...
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1answer
151 views

Feature Selection - Mutual Information with response variable that takes three values

I am trying to calculate Mutual Information scores for Feature Selection. I have successfully implemented the Mutual Information to test each feature against the binary response variable. Each ...
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1answer
146 views

Interpretation C value in linear SVM

My C value is very low (close to 0). Does this mean that my feature (dimensions) have no real separative (and thus predictive) value? (As the SVM basically chooses to ignore the training data ...
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386 views

What do the variables mean in the SVM objective function?

For a linear SVM, the documentation tells me the formula is: $$\frac{1}{2}w^Tw + C\sum_{i=1}^l\xi_i.$$ Please explain to me in layman's terms what $w$ and $ξ$ represent. Is w the distance to the ...
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27 views

Should the 'TPR of adaboost' be better than base classifiers'?

There are two different base classifiers which produce true positive rate (TPR) values 99.46% and 91.79%. When I use these base classifiers in adaboost, what should the new TPR be? Better than two of ...
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124 views

Multi-armed bandit in face of full reward information

I am new to this area of machine learning. I am just walking myself through UCB1 algorithm which seems to assume that the payoff can be learnt only for action that ...