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

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4
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2answers
70 views

What is a log-odds distribution?

I am reading a textbook on machine learning (Data Mining by Witten, et al., 2011) and came across this passage: ... Moreover, different distributions can be used. Although the normal ...
0
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0answers
6 views

Using t-test for feature selection after z-scoring data?

Suppose I have a high-dimensional dataset, and a binary classification problem. I want to use the two-sample t-test for feature selection. If the data has been normalized by z-scoring (so it has zero ...
0
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0answers
11 views

What is divergence in indicators/oscillators?

What is the direct cause of divergence in indicators/oscillators? Is it because of using the close price? How could I filter it out or isolate it? This would be useful to extract for a predictive ...
0
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0answers
18 views

(binary) Matrix completion with less known data

Recently, I meet such problems, I call it matrix completion problem. For example, the row denotes the users and the column denotes items. And If one user like the ...
0
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0answers
8 views

How do I install and use Shogun toolbox in matlab? [on hold]

I want to use Shogun toolbox in matlab (windows) but their installation guide is not clear. Does anyone know simple steps to install shogun toolbox in matlab?
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0answers
10 views

The Shogun Machine Learning toolbox for SVM with precomputed kernel and zero bias

Can I use the Shogun Machine Learning toolbox for SVM with precomputed kernel and zero bias. I should be able to input pre-computed kernel and I also should be able to set bias zero.
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0answers
8 views

SVM with pre-computed kernel and zero bias

I have an optimization function, where I need to give my own kernel matrix and bias value is zero. The kernel matrix is calculated using the data but there is no specific formula for it. If I have a ...
0
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2answers
69 views

Machine Learning and Biostatistics [on hold]

I am interested in a few areas with biostatistics, and was reading the course catalog at a university that offers machine learning. I am taking topology now, and i think machine learning uses ...
0
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0answers
53 views

Probabilistic model Vs Weight based model

Can someone please explain what is the difference between probabilistic model vs weight based model with respect to ML context. When to use one over the other?
1
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1answer
30 views

Multi Output Neural Networks

Up until know I only used neural networks to classify a single output, I set one output neuron for each class and check which neuron has the highest/lowest activation. What I am trying to do is to ...
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0answers
23 views

Neural Network for hand written digit recognition

I have create the neural network with three layers. 1 layer - 500 inputs 2 layer - 500 inputs 3 layer - 10 output classes. I have synthesized the ...
1
vote
1answer
31 views

Glmnet Caret Package with small number of observations

I have a regression problem where I’m attempting to train a data set with 70 predictors, but only 35 observations with glmnet in the caret package. I’m trying to determine the best resampling method. ...
1
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2answers
37 views

Time Series Anomaly Detection with Python

I need to implement anomaly detection on several time-series datasets. I've never done this before and was hoping for some advice. I'm very comfortable with python, so I would prefer the solution be ...
-1
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0answers
13 views

How to create a feature vector with scikit-learn? [on hold]

I have extracted some bigrams from a corpus, how can i create a feature vector with those bigrams with scikit-learn?, could anybody provide me some example?. Thanks
2
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0answers
9 views

Why does glmnet in caret give different predictions for different alphas even though lambda is zero?

In R, when using caret to train an elastic net regularization model, I find that different values of alpha give different predictions when the lambda parameter equals zero. This should not be the ...
3
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0answers
12 views

How to measure when error stabilizes (convergence) on Random Forests (or, when do I stop training)

I'm doing an implementation of Random Forests. As I was the original paper (page 11) and this nice book on the subject (15.3.1, page 592), they mention that when the out-of-bags error stabilizes (when ...
1
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1answer
37 views

What is considered to be “good” classification rate?

Let's say I am trying to figure out whether two classes can be differentiated. My methods may not be perfect, but I would like to know whether my features "mean" anything that may possibly be added to ...
1
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0answers
16 views

How do we get/define filters in convolutional neural networks?

How do i obtain filters from convulutional neural network(CNN)? My idea is something like this: Do random images of the input images (28x28) and get random patches (8x8). Then use autoencoders to ...
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0answers
3 views

How to account for different ratio of samples during training and detection using a support vector machine (svm)?

Consider the following object recognition case: Detection of objects in an image using a sliding window approach in combination with a svm model. During sliding window search using multiple scale ...
0
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0answers
9 views

How to find a good model for an object recognition case using a support vector machine (svm)?

Consider the following example of an object recognition case: I'm trying to detect objects in an image using histograms of oriented gradients (hog) features. The feature vector resulting from hog is ...
1
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0answers
28 views

Building a predictive model, regression with a long right tail

I am trying to build a, regressive, predictive model for a target time-series that is heavily skewed. You could think of the target as being like earthquake magnitudes or heavy rainfall. Most of the ...
1
vote
1answer
33 views

Does Deep network (e.g. # of hidden layer=2) always better than shallow network (i.e. # of hidden layer=1)?

I attempted to build a deep network (e.g. deep autoencoder) for some object classification, my result showed that the deep networks is worst than shallow network. However, from what I have read from ...
0
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0answers
20 views

How to CORRECT un-reliable and un-stability in the prediction results

Currently, I meet such questions when building Random Forest model using my data set. My full data set: X_lab: 839 * 469 and y_lab: 839 * 1 which is for all labelled data and X_unl: 20346 * 469 which ...
0
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0answers
9 views

Practical problem computing de k-nearest neighbors in CF?

I’m trying to apply de knn to a very dynamic system where users (like/dislike) items very frequently and new items became available all the time. My question is how often should the algorithm ...
0
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0answers
14 views

Regarding Naive Bayes and conditional independence

We all have been talking about how Naive Bayes may, in some cases, not perform well due to the fact that this assumes conditional independence of features and MOSTLY, this is not true for real world ...
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0answers
26 views

What is the relationship between vector space models & support vector machines?

Is there a relation between them? Specifically, if I have a VSM can I classify it through SVM?
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0answers
16 views

Graphically, how does the non-linear activation function project the input onto the classification space?

I am finding a very hard time to visualize how the activation function actually manages to classify non-linearly separable training data sets. Why does the activation function (e.g tanh function) ...
0
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0answers
7 views

LIBSVM for pre-computed kernel and zero bias (b values is zero)

I want to do binary classification and I'm using LIBSVM library for that. I have a precomputed Kernel and my bias value (b) is zero. Can I do this in LIBSVM or do I have to use some other library? ...
0
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0answers
27 views

Visualize a large binary matrix with instances and three classes

I have a matrix with 9500 columns and 1000 rows. Each row represents an instance. I have three classes and an instance belongs to a class. Each column represents a binary feature. That is, each cell ...
0
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0answers
8 views

Meta-parameter search for elastic net regularization of general objective function

In their 2004 paper on elastic net regularization, Zou and Hastie present an efficient method for finding the meta-parameters by folding the $L_2$-regularization component into the OLS problem and ...
0
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0answers
20 views

Does Text Classification using SVM needs to use a dictionary to build the features vectors?

I'm kinda new to this, but I want to do an experiment I need your guys help. Open to all suggestions. Let's say I have around 5000 user accounts for which I only have several attributes [first name, ...
0
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0answers
14 views

algorithm to predict cost function

The goal of problem is to predict the weight for missing data . I have a dataset of categorical type as shown below ...
0
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2answers
47 views

Different results from several “passes” of Random Forest on same dataset

I've been playing around with the German Credit dataset available in Kuhn & Johnson's caret package for ...
1
vote
1answer
25 views

How to apply the output layer function in a neural network

I am implementing a Neural Network in a somewhat different fashion. I train my neural network locally using a small subset, and export the weights. My goal is to test the neural network in a ...
0
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0answers
34 views

Stochastic Gradient Descent for Logistic Regression always returns a cost of Inf and weight vector never gets any closer

I am trying to implement a logistic regression solver in MATLAB and i am finding the weights by stochastic gradient descent. I am running into a problem where my data seems to produce an infinite ...
0
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1answer
40 views

What is the convex hull in ROC curve?

I'm reading a paper about ROC and PR curves. They mentioned the ROC convex hull but they don't define it or say what it is. Can someone please tell me the meaning of it? What is a convex hull in ROC ...
2
votes
2answers
42 views

An intuitive meaning of the area under the PR curve?

Wikipedia says that an interpretation of the area under the ROC curve is: "the area under the curve is equal to the probability that a classifier will rank a randomly chosen positive instance higher ...
4
votes
2answers
200 views

How to do exploratory data analysis to choose appropriate machine learning algorithm

We are studying machine learning via Machine Learning: A Probabilistic Perspective (Kevin Murphy). While the text explains the theoretical foundation of each algorithm, it rarely says in which case ...
0
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0answers
13 views

Usage of libsvm with RBF kernel and no Offset

I'm using libsvm for the binary classification and using a precomputed Kernel. In my particular problem there is no bias term (it's zero). Is there anyway to adjust the bias term in libsvm (and not ...
-1
votes
1answer
26 views

Apriori Algorithm Support Calculation [closed]

Why Apriori functions in arules library in R returns different values that what it should be? The sample data in the pic below. Each line between {} means that it is a single transaction. So ...
2
votes
0answers
24 views

R programming, correlation of quantitave variables with one qualitative variable

I have a flat CSV file that has one column of student names, one column of grades (outcomes) coded as a factor A-F, and about 100 columns of test scores (independent variables) of various sorts, on ...
2
votes
2answers
138 views

Gaussian noise model derivation

I have the following linear regression model, $y = f(x;w) + n$, where $y$ is the vector of true labels, $x$ is the observed data, $f(x;w) = w^Tx$, and $n$ ~ $N(0, \sigma^2)$ is the noise. Why then ...
1
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0answers
19 views

Using standard machine learning tools on left-censored data

I'm developing a forecasting application whose purpose is to allow an importer to forecast demand for its products from its customer network of distributors. Sales figures are a pretty good proxy for ...
1
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2answers
30 views

What does “permutation invariant” mean?

I have seen a term "permutation invariant" version of the MNIST digit recognition task. What does it mean?
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2answers
56 views

What're the differences between PCA and autoencoder?

Both PCA and autoencoder can do demension reduction, so what are the difference between them? In what situation I should use one over another?
1
vote
1answer
40 views

I am having trouble understanding (and implementing) logistic regression for classifying into three classes

(For reference, i am using Kevin P Murphy's Book "Machine Learning: A Probabilistic Perspective" and implementing with MATLAN - without any toolboxes) I have a dataset with 392 samples (rows), each ...
0
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0answers
19 views

How to compute F-statistics for each features of regression models in glmnet?

I have learned lot's of Lasso regression models(20000) using glmnet. I need to compute somehow test statistics for each features of models. like F-statistics,... Can I do this using bootstrapping ? ...
0
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0answers
21 views

How to improve linear model generalization when autocorrelation is present?

I have features $X_t$ and response $Y_t$ (all continuous variables) and my objective is to find the best estimate of $f(X_t)=Y_t$ where $f$ is linear, and 'best' is defined as lowest generalisation ...
2
votes
1answer
23 views

What does pre-training mean in deep autoencoder?

I am confused by the term "pre-training". What does it mean in deep autoencoder? And how does it help improving the performance of autoencoder? (I know this term comes from Hinton 2006's paper: ...
0
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1answer
16 views

What is the toolkit that implements Cost sensitive Support Vector Machine?

I need implementation of cost sensitive support vector machine. The cost is different for each training example (unlike each class). So problem is to solve $max_\alpha$ $-1/2 \sum_{i,j} ...