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

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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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1answer
6 views

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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1answer
19 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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1answer
25 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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1answer
18 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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1answer
33 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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0answers
29 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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1answer
17 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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7answers
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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 2 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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9 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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9 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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26 views

10 fold cross validation model in weka

down vote favorite I am new to Weka. Trying to build a specific Neural Network arcitecture and testing it using 10 fold cross validation of a dataset. Now building the model is a tedious job and ...
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14 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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1answer
49 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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1answer
16 views

Regression-tree Tuning in a Streaming Setting

Some time ago I went through a NIPS 2013 paper Regression-tree Tuning in a Streaming Setting. The paper proposes a tree-based regressor. Is there any implementation of this algorithm available? (At ...
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1answer
22 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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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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1answer
36 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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2answers
114 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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0answers
13 views

Error in eval(expr, envir, enclos) : object 'Case' not found in train( method=rpart) in caret package of R [on hold]

I am trying to fit my training data for Coursera Practical Machine Learning Quiz 2. ...
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1answer
18 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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1answer
15 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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0answers
13 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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12 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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1answer
24 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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1answer
13 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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3answers
44 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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1answer
59 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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1answer
64 views

How do you validate your machine learning models?

I am wondering what approaches are commonly used for validating a classification or prediction models: Approaches that am using at the moment: Using truth-sets: - ROCs, Bootstrapping, Accuracy, ...
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15 views

Forensics in wireless networks, anomaly detection and beyond?

first i'de like to apologize if this is not the right place. Next year i'm gonna be working on my final project in computer security, i have to build a wireless forensics tool that can analyse a data ...
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1answer
21 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
26 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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1answer
49 views

Simple SVM Question

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

large variables and low sample (p > n) problem: ridge , LASSO, PLS, PCR which is most suitable for predictions

I am trying see whether to go for ridge regression, LASSO or principal component regression (PCR) or Partial Least Squares (PLS) in a situation where there are large number of variables / features (p) ...
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13 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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1answer
27 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 ...
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1answer
41 views

How does cross-validation and the Bayesian method overcome the overfitting problem?

I was told that cross-validation and the Bayesian method can overcome the overfitting problem. I was told that comparison of models in a Bayesian way is actually doing cross-validation... What is the ...
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19 views

Why do we need nested cross-validation for parameter selection?

A normal cross-validation finds the best parameters such that: for each parameters: cross_validate(algorithm(parameters)) model = Use Best Model Why do ...
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0answers
8 views

FNN package for R - possible to use manhattan or other distance metrics? [closed]

It is possible to change the algorithm, but I am stuck trying to change the distance metric. I know it usually makes little difference, but in this case I need it!
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14 views

Feature learning with a deep learning aproach?

How to create a feature vector from text with a deep learning aproach?. Im new at this topic, could anybody advice me where to start and how to aproach this task?.
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1answer
115 views
+50

Bayesian lasso vs ordinary lasso

Different implementation software are available for lasso. I know a lot discussed about bayesian approach vs frequentist approach in different forums. My question is very specific to lasso - What are ...
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1answer
24 views

How to : a brief intro to scaling and rescaling data ( inputs) for supervised learning algorithms

I understand the concept of scaling and that it improves results in SVM's and NN's. however I would like to find somewhere where is is explained, in easy "layman's terms" terms. of how it is done. I ...
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30 views

What are appropriate validation methods for a Bayesian network model with low sample size?

I am currently using a Bayesian network model with 20 variables and 210 data points, with 15 locations measured at 14 different time points each. There are also some restrictions on what types of ...
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2answers
32 views

What model would be appropriate for predicting electrical consumption given multiple (mostly) independent variables?

I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including: daily temperature (continuous) hours ...
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16 views

Softmax regression or $K$ binary logistic regression

For a multi-class classification problem, we can use $K$ binary logistic classifiers, or one softmax regression classifier, so how to make the choice between the two? IMHO, the $K$ binary logistic ...
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7 views

Use a different loss function for cross validation in liblinear

I am trying to learn a L2 regularized Logistic regression model in liblinear. I need a way to specify the C parameter which I do by cross validation. However, the loss/accuracy measure in cross ...
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1answer
33 views

Approach for mapping consumer preferences

I have this web application where I need to map consumer preferences based on some input information and individual choices. My goal is to create a list of product recommendations and evaluate the ...
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0answers
6 views

Specifying the validation dataset for liblinear

I am trying to use the liblinear logistic regression model with L2 regularization. I don't want the training data to be splitted for the cross validation. I want to specify my own validation set for ...
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25 views

In Gaussian Processes, how to understand the hyper-parameters optimization?

I know that in a GP, hyper-parameters are optimized by maximizing the marginal likelihood. Could anyone explain this method to me please? Thanks for your help in advance.
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How to understand the log marginal likelihood of a Gaussian Process?

I'm trying to understand Gaussian Processes. Could anyone tell me: Why we need to use the log marginal likelihood? Why using log, the marginal likelihood can be decomposed to 3 terms (including a ...