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

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Test for autocorrelated variables

I would like to know if there is a good non-parametric test for detecting auto-correlation of one variable between all the observations of my dataset. I have 60 predictor variables for a statistical ...
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1answer
29 views

How to make a trained neural network “forget” an instance?

I am using neural networks for predicting the behavior of a dynamic system. A neural network is trained online using snapshots from the system's past. The system changes its state at irregular ...
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13 views

Anomaly Detection with very small number of positives [closed]

I am trying to detect anomalies in a population comprising of 10 features and around 90,000 observations. Past investigations have revealed 18 positives. Given limited data for supervised learning, I ...
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2answers
74 views

Is dimensionality reduction almost always useful for classification?

Is singular value decomposition almost always useful in practice for enhancing the predicative power of a trained classification model? E.x. A dataset for classification has 20,000 features. Run SVD ...
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36 views

using Cross Validation in matlab with neural networks

I want to make a cross validation on neural network, I tried to pass the labels to crossval function, with the help of ...
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1answer
26 views

What are the techniques to deal with classifying sparse categorical features?

Suppose I have a group of features each one is sparse with a few number of values (1-10) what are the required preprocessing steps required to avoid degradation of the performance of the classifier ...
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124 views

Machine Learning

I have been working on some self study "machine learning". Based on a few posts here, I wanted to make a program that "learned" via Bayes Law. I test it with some simple truth tables. It recalls the ...
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1answer
26 views

Cumulative match score

I have seen loads of graphs in papers of cumulative match scoring, but I can't find any information about what it means, or how it is created. A context that would be useful to see the explanation ...
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16 views

Generating a good training dataset for decision tree building

I am building a decision tree predicting the accuracy score of an image processing algorithm, based on a number of image parameters. I have identified 6 uncorrelated parameters that impact the ...
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24 views

Learning user behavior that changes over time

I am learning a model using SVM that will predict user behavior of some kind. Simplifying this model, each example in the feature space contains some features: $f_1,f_2,..,f_n$ and a class $a$ that ...
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38 views

Has there been a project to apply machine learning to generation of indices for books?

Generating an index for a textbook is a tedious task. Can one automate it with machine learning? Are there any references to previous attempts in the literature to do this?
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49 views

Semi-supervised classification with Rmixmod package in R

Semi-supervised classification with Rmixmod package in R. http://math.univ-lille1.fr/~biernack/index_files/articleJSS.pdf , a document on the Rmixmod R package, provides mathematical exposition of ...
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1answer
44 views

SVM in R (package e1071): predicting class using predict()

I have difficulties to understand predict.svm. Please find an illustration of my confusion below. As we can see, results are different depending on the ...
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1answer
39 views

Expectation Maximisation

I'm currently reading Thomas Hofmamms paper on Probabilistic Latent Semantic Analysis. He includes a formula for the E step in Expectation Maximisation, but has proposed an alternative to this step ...
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1answer
29 views

Zero-centering the testing set after PCA on the training set

I have a training set of data on which I do principal components analysis (PCA) and save the loadings/eigenvectors/coefficient matrix. I want to use the eigenvectors to transform my testing data into ...
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8 views

How do i get prediction accuracy when testing unknown data on a saved model in Scikit-Learn?

i have a model i have trained for binary classification, i now want to use it to predict unknown class elements. ...
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0answers
9 views

Blocks of variable size in k-fold cross-validation

I would like to make a custom k-fold cross-validation method for my data, by generating folds of auto-correlated observations. This would create many folds of variable size for test errors as well as ...
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1answer
15 views

Feature selection in GBM

I am using gradient boosting (caret package in R). As far as I understand, the feature selection is already included in this package. However, I slightly ...
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1answer
35 views

Using Adaptive Linear Neurons (Adalines) and Perceptrons for 0-1 class problems

I am wondering how to adjust the Adaline algorithm to classify the classes 0 and 1 instead of -1 and 1. I found a section in Neural Networks and Statistical Learning by Ke-Lin Du, M. N. S. Swamy ...
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19 views

Observation Likelihood in hidden Markov models

As far as I understand, in discrete HMM, the observation symbol probability distribution $b_{i}(O_{t})$ is always a probability less than 1, e.g. $\frac{1}{6}$ for each side when rolling a dice. But ...
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2answers
109 views

Ax = b. How can I estimate A, given multiple data vectors of x and b?

I have a problem and I believe there must be a machine learning technique to solve it, but I am new to machine learning and I have no idea where to start. So, I have multiple multivariate parameter ...
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32 views

The accuracy measure of a classification process

I have build the signal processing and feature extraction models, those features, inputed to Neural Network using matlab, which is give me the following performance measure,And I have several ...
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37 views

Regularized regression with missing data?

Are you aware of any regularized regression methods (i.e. Lasso, elastic net) which allows for using cases with incomplete (missing) data (e.g. using EM estimation)? And if yes, is the method ...
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18 views

Good library for Switching Autoregressive Hidden Markov Model (SAR-HMM)

can you recommend a good library for SAR-HMM? (Switching Autoregressive Hidden Markov Model) Apparently only MATLAB can be recommended. ...
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11 views

converting discrete values to buckets to perform predictions

I have a set of continuous discrete values, which I would like to convert to a classification task. Say, my scores in an exam are anything between 0-100. I want to convert my scores in the next exam ...
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1answer
141 views

Do I need to be a mathematician to be more than an expert in machine learning? [closed]

Machine learning has its roots principally in mathematics so if I wanted to be not only an expert but an innovator in this area would I have to be a computer scientist or would I need to be a ...
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1answer
40 views

Random forest regression on a given interval

I'm training a random forest regression model on a dataset that consinsts of values in the range of 0-50. It has many values close to zero and only 500 observations. The R^2 is also small, about ...
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1answer
158 views

A framework for multi-valued categorical attributes

In the scenario in which I'm working each entity could be represented in terms of N distinct properties that I will call p1, p2, ..., pn. For each of them, an entity, can have its specific range of ...
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24 views

Book recommendation of Time series analysis [duplicate]

I am doing research in data mining, i am not sure if this course (time series analysis) gonna help me in my research. I am almost new to statistics and i know a little about this field, so do you ...
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1answer
16 views

difference between stochastic gradient descent and batch

I've watched the canonical Andrew Ng video on the subject but I'm trying to translate those concepts into java, and I'm not quite sure I did it right. The thing that's confusing me is, I made a toy ...
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17 views

Bayesian Ridge vs Stochastic Gradient Descent

I was running some Regression algorithms on a dataset and it just so happens, that the Bayesian ridge Regression techniques is performing not so well as the SGD (Stochastic Gradient Descent) ...
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24 views

Implementing WARP Loss (Gradient Computation)

I am trying to implement the WARP Loss in Torch, as defined in the WSABIE paper: http://www.thespermwhale.com/jaseweston/papers/wsabie-ijcai.pdf The Algorithm is as follows: The Algorithm ...
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2answers
59 views

Performance in training set worse than in test set?

I have a high dimensional regression problem where I used glmnet to solve this. A nested CV scheme is used. In the inner CV loop (10x5 fold) a grid search is done to find the optimal hyperparameters ...
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14 views

Query re. how to set up an SVM, which SVM variation … and how to define a metric

I’d like to learn how best set up a Support Vector Machine for my particular problem (or if indeed there is a more appropriate algorithm). My goal is to receive a weighting of how well an input set ...
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12 views

Number of hidden units in Restricted Boltzmann Machine?

In section 12.1 of Geoff Hinton's Practical Guide to Training RBM on how to choose the number of hidden units it is stated that one should ...
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24 views

Why does the equation $ -\sum^{n}_{t=1} \tilde{W}(t)_{m-1} y_{t}h(x; \theta_{m}) = 2 \epsilon_m -1$ hold in boosting?

I was trying to understand the boosting algorithm as described by the MIT graduate class lectures notes on ocw. On page 2 they give the outline of boosting as follows: The step that is not clear ...
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1answer
111 views

How do you mathematically prove that boosting cannot have zero error in training set arranged in a square with the corners labeled plus and minus?

I have the following data: Say we want to perfectly separate those points using only an ensemble of horizontal and vertical decision stumps. Maybe using Boosting or Adaboost, but the main point is ...
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28 views

Decomposing the non-deterministic transition functions in non-Markov decision processes into several deterministic transition functions

Problems in reinforcement learning are commonly modeled as Markov decision processes (MDPs). One essential part of MDPs is the transition function $T: S \times A \times S \rightarrow [0, 1] \in ...
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21 views

Adding additional data to a model

I'm currently running a logistic regression for classification on 100 patients dataset. However for 10 of those patients I have additional data that would help the accuracy for for those 10 patients. ...
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22 views

Hyper parameters optimization

Any one with a tip on how to define a suitable condition to find an optimal set of parameters for a combined norm regularization on a grid? Not cross validation or any related method. I am asking if ...
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1answer
52 views

Boosted trees and Variable Interactions

How can one see in a Boosted trees classification model, which variables interact with each other and how much? I would like to make use o this in R gbm package if possible
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1answer
21 views

What are the good study materials on Association Rules?

I am looking to learn Association Rules, from basic level. I was looking for some good web based materials to start with. My objectives in the materials is to: (a) learn the aspect nicely from ...
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1answer
24 views

Practical collaborative filtering application for large database

I’m designing an item-based collaborative filtering for a large database with over 100,000 items. My question is how the whole process works in practice since the algorithm takes a long time to ...
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2answers
87 views

Introductory multivariate statistics reference for beginners

I am from computer science department doing research in data mining and image mining. I remember the last course about stat was introductory to statistics and probability in general. Now I have this ...
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1answer
45 views

Non linearity in data using regression

I have been working with a set of data which is set in an engineering discipline. However my aim is predictive in nature, i.e., I need to get the relationship between the parameters as well as predict ...
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12 views

How to draw ROC curve for softmax classifier?

As softmax classifer is a generalized form of logistic regression, how to draw ROC curve for softmax classifier?
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422 views

What are the theoretical guarantees of bagging

I've (approximately) heard that: bagging is a technique to reduce the variance of an predictor/estimator/learning algorithm. However, I have never seen a formal mathematical proof of this ...
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0answers
18 views

Value of the loss function and Classification Errors in gbm package (R)

I have a simple problem of classification (0s and 1s) using adaboost loss function. When I check the components of a boosted model using the gbm package I see: ...
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4 views

Lower bound for the randomized perceptron algorithm

The above exercise is taken from the lecture notes:http://goo.gl/tlkpKc It is a kind of lower bound for the perceptron linear classification algorithm. I was able to show a sequence ...
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1answer
93 views

The difference between logistic regression and support vector machines?

I know that logistic regression finds a hyperplane that separates the training samples. I also know that Support vector machines finds the hyperplane with the maximum margin. My question: is the ...