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

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How Spark MLlib: how does the LogisticRegressionWithLBFGS work with discrete variables?

How does the logistic regression model on Spark MLlib (LogisticRegressionWithLBFGS) work with discrete variables (for example sex,race, or indicator variables which ...
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0answers
28 views

How to combine weak classfiers to get a strong one?

Let as assume that we have a binary classification problem. We also have several classifiers. Instead of assigning a vector to a class (0 or 1) each classifier returns a probability that a given ...
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1answer
17 views

Robust softmax solutions for Theano?

I am implementing multilayer perceptrons with the softmax activation function over Theano. In some extreme cases I am running into problems with too high/low values in the softmax function that ...
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0answers
7 views

How to input sparse feature in theano

In theano everything is symbolic, so how to input sparse feature in , for example, neural network? The setting is: the task is text application. the input is a mini-batch. Since theano sparse module ...
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1answer
18 views

Guessing the word from context

Can I train a system to decide, which one of suggested words is more likely to appear in the sentence being analyzed? For example, if I have sentence "I was playing with my ______ when I heard the ...
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2answers
81 views

Which statistical or machine learning library to use for this custom hierarchical/agglomerative clustering approach and How? [on hold]

I have a distance function, which takes two strings and returns their edit distance (integer) (e.g in my case its levenstein distance function which takes two strings and return their edit distance). ...
2
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1answer
31 views

Random forest vs Adaboost

In section 7 of the paper Random Forests (Breiman, 1999), the author states the following conjecture: "Adaboost is a Random Forest". Has anyone proved, or disproved this? What has been done to prove ...
2
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1answer
148 views

Representation within a RKHS framework

Given a p.s.d kernel $Q$, can minimization/maximization of $Tr(X^TQX)$ over X be represented within a reproducing kernel Hilbert space (RKHS) framework? If there is a primary concern with the trace ...
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1answer
16 views

What is the predictive distribution of Bayesian supervised Learning? (rigorous argument)

I was trying to understand the posterior predictive distribution for any supervised predictor (by that I mean any classifier or regression predictor $f$). The exact equation I am unsure of is: $$ ...
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1answer
67 views

Pre-processing (center, scale, impute) among training sets (different forms) and the test set - what is a good approach?

I am currently working on a multi-class classification problem with a large training set. However, it has some specific characteristics, which induced me to experiment with it, resulting in few ...
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2answers
80 views

What does the term “Estimation error” mean?

I was reading some notes on machine learning when I came across the following sentence: First, we may have a large estimation error. This means that, even if the true relationship between x and ...
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0answers
31 views

Decision boundary in multivariate naive Bayes

This is from a sample exam for which I do not have the solutions. The question as stated is: True or False: The multivariate Gaussian naive Bayes always has a linear decision boundary. Explain ...
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0answers
14 views

SVM parameter tuning for unbalanced classes (with class weights)

I am trying to run an SVM on an imbalanced dataset (0-90%, 1-10%) using the e1071 package, with the radial kernel. I am using cross-validation to select the best gamma and cost. Additionally, I want ...
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0answers
20 views

Most efficient SVM implementation

I'm currently using LIBLINEAR to perform linear SVM on a very large data set that sometimes collapses. Is there a more efficient implmentation of SVM? UPDATE: The C version of liblinear collapses, ...
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3answers
83 views

Do we still need to do feature selection while using Regularization algorithms?

I have one question with respect to need to use feature selection methods (Random forests feature importance value or Univariate feature selection methods etc) before running a statistical learning ...
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0answers
21 views

My Cross-validation error is always increasing with increasing regularisation parameter

I am not sure what is happening, but my cross-validaton error is always increasing with increasing alpha in ridge regression. It should technically go down and then increase. Here is what I am doing ...
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1answer
30 views

Classifer for unbalanced dataset?

Is there any classifer that can natively support unbalanced datasets? Or what best practices you can suggest to handle such datasets? For example I want to solve task called "pedestrian detection" ...
2
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1answer
195 views

Does the product of two p.s.d kernel matrices result in a kernel matrix?

In a ML setting, where $a_1,..., a_n$ are a set of training points. A kernel function is a function $κ$ that gives the inner product between two vectors in the feature space: $κ(a_i, a_j ) = ψ(a_i) · ...
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1answer
941 views

The relationship between the number of support vectors and the number of features

I ran an SVM against a given data set, and made the following observation: If I change the number of features for building the classifier, the number of resulting support vectors will also be changed. ...
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1answer
187 views

Possible reason for failing to build a support vector machine

I was trying to build a classifier for a set of documents using a support vector machine. I choose to build the feature space using term occurrence. While experimenting, I found the following ...
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34 views

How to use images as input in machine learning?

There are different machine learning algorithms (neural networks, decision trees, support vector machines and so on). Usually we assume that there is a mapping from vectors to nominal or numeric ...
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1answer
30 views

Reconciling boosted regression trees (BRT), generalized boosted models (GBM), and gradient boosting machine (GBM)

Questions: What is the difference(s) between boosted regression trees (BRT) and generalized boosted models (GBM)? Can they be used interchangeably? Is one a specific form of the other? Why did ...
2
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1answer
21 views
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6answers
4k views

Variable selection procedure for binary classification

What are the variable/feature selection that you prefer for binary classification when there are many more variables/feature than observations in the learning set? The aim here is to discuss what is ...
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0answers
6 views

Merging two different segmentation solutions into one

I have the following problem: two different segmentation analysis to do, one using needs/motivations for consuming a product and one related to general attitudes toward product category and lifestyle. ...
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0answers
11 views

Why does value iteration converge? [on hold]

For Markov Decision Processes, why does value iteration converge?
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1answer
87 views

Neural Network Process Question - Updating weights after each training set

When creating a neural network, do I update the weights after each run of forward then back propogation? Or do I just keep the random weights and update the Delta variables? I am looking at slide 8 ...
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45 views

Machine Learning: curve completion using sets of completed curves

I am very new to the world of machine learning and i am wondering if a) machine learning is able to solve the problem b) whats the best way to do it (pref with example) I have a set of curves for a ...
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0answers
5 views

best method for calculating sales for keywords

I have a set of keywords and searches made on those keywords. Each keyword search has produces a number of products. Knowing how many units each product sells, and hypothetically knowing every ...
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4answers
2k views

Are there any tutorials on Bayesian probability theory or graphical models by example?

I've seen references to learning Bayesian probability theory in R, and I was wondering if there is more like this, perhaps specifically in Python? Geared towards learning Bayesian probability theory, ...
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0answers
15 views

Looking for a principled/systematic procedure for discarding features

I have a collection of $M_i \times N$ matrices $X_i$ whose rows are (raw) feature vectors (from a common $N$-dimensional feature space). MATLAB reports that most of the covariance matrices $C_i := ...
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0answers
10 views

Difference between Bag of words and Vector space model

I am searching for the intuitive difference between Bag-of-words and vector space model? Is there any relationship exists between bag-of-words and vector space model. I tried searching but couldn't ...
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2answers
229 views

Newbie to neural networks

Just starting to play around with Neural Networks for fun after playing with some basic linear regression. I am an English teacher so don't have a math background and trying to read a book on this ...
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1answer
90 views

random forest modelling with high dimensional data

I am puzzling on developing random forest regression of high dimensional data. My predicted variable is plant cultivar or Class (say 1, 2, 3) and regresser are 82 variable in separate column (40 X 83) ...
7
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4answers
2k views

Support vector machine for text classification

I am currently having a data set, class 1 with about 8000 short text files and class 2 with about 3000 short text files. I applied LibSVM and tried a couple of parameter combinations in the ...
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1answer
49 views

MLP: Classification vs. Regression

Abstract I am teaching myself about NNs for a summer research project by following an MLP tutorial which classifies the MNIST handwriting database. I want to change the MLP from classification to ...
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1answer
18 views

Learning a model which can fit the training data accurately

I am using weka for creating a model on a training set for a classification task. I am trying different classifiers for this. But when I try to give one of the data points which are present in the ...
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1answer
24 views

Can boosting be thought of as a genetic algorithm?

Can boosting be classified as a genetic algorithm or as an instance of simulated annealing? Or, is it a completely different paradigm? Essentially, I'm trying to rectify discrete optimization ...
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2answers
77 views

Maximally reducing the rank of a matrix by removing some rows or columns

I have a $N \times M$ matrix, and the rank of matrix, $r$, is near $\min(M,N)$. I want to minimize the rank by removing some of the rows or columns to get $r \ll \min(M,N)$. The goal is to achieve ...
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1answer
41 views

Treating missing data in voting pattern analysis

I'm trying to analyze voting patterns of Ukraine's parliament deputies. I scraped all the data on their voting during last session. Each data entry has following information: Deputy name, date, bill ...
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0answers
35 views

Using variance in place of standard deviation for z-normalization

I'm implementing a 1-nearest neighbor (with dynamic time warping as the distance measure) classification algorithm on a severely constrained embedded platform with no FPU, so we're doing fixed point ...
1
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1answer
14 views

How to interpret concretely the misclassification error?

I'm reading about Cart classification with rpart on R, and after all we should compute the misclassification error, given that y is the column that stocks classes, and x is the variable columns and ...
0
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1answer
131 views

How to solve this problem on Curse of Dimensionality problem - Nearest Neighbours

I have started learning classification techniques and trying to solve the problems from the book Introduction to Statistical Learning. While currently working on the which is based on Curse of ...
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1answer
82 views

Handling Missing Values During Test Phase

I was searching for methods for handling missing values in case of Regression task. There are already few threads but I couldn't find what I was looking for. Suppose I have 4 independent categorical ...
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1answer
53 views

About writing a machine learning paper

I applied support vector machines to a relatively small dataset. I used relatively simple techniques, and achieved publishable results. Now, when already writing the paper, I got an idea, which would ...
2
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0answers
388 views

Regarding kernel-based naive Bayesian classifier

Are there any good references for kernel-based Naive Bayesian classifier?
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1answer
19 views

How to do crossover and mutation in one GA iteration process?

I am learning genetic algorithms. I am trying to demonstrate one GA interation process for the problem as follows: X, Y and Z are the three integer variables ranges between 0 to 3, and there are ...
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0answers
9 views

Which model approach for data with timings and signals

I have a data set of times, signals and their values. The signals have values from A1 to A6. The first 25 data points of the record are as follows: ...
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0answers
12 views

Auto tune PID loop using linear regression

I am sure most of you are familiar with PID loop. Let look at an example to help me explain what I want to do. Let say I want to tune a temperature control PID, for home air conditioning. My apartment ...
0
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2answers
612 views

Model-based learning algorithm for recommendation engine

Can you please suggest me a good model-based learning algorithm to recommend items to the user? Is there any open source implementation available on model based learning algorithm? I am sure Apache ...