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

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How can get F-measure, precision and re call and also Cluster labels in R

I am new to R. I want to use hierarchical clustering of R to cluster following distance matrix. ...
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Translating machine learning problem into regression framework

Suppose I have a panel of explanatory variables $X_{it}$, for $i = 1 ... N$, $t = 1 ... T$, as well as a vector of binary outcome dependent variables $Y_{iT}$. So $Y$ is only observed at the final ...
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335 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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81 views

X-means algorithm and BIC

I want to simulate X-means algorithm based on [1] in MATLAB. I have some questions about this algorithm. X-means Algorithm Steps: (1) Initialize K = Kmin. (2) Run K-means algorithm. (3) FOR k = ...
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130 views

Random Forest - What training set measure is the best predictor of test set accuracy?

I'm running a random forest model on a training sample in R in order to make predictions on a hidden test set. I'm having difficulty in understanding how I should go about improving my model in order ...
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17 views

Merging observations in Gaussian Process

I am using Gaussian process (GP) for regression. In my problem it is quite common for two or more data points $\vec{x}^{(1)},\vec{x}^{(2)},\ldots$ to be close to each other, relatively to the length ...
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How would you modify your model according to that confusion matrix?

How would you proceed with your model, if the confusion matrix looks like on the picture below? Classes 2, 3 and 4 get misclassified a lot among each other. EDIT: The Confusion Matrix was created ...
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2answers
71 views

How do gradients propagate in an unrolled recurrent neural network?

I'm trying to understand how rnn's can be used to predict sequences by working through a simple example. Here is my simple network, consisting of one input, one hidden neuron, and one output: The ...
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35 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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Why my Convolutional Neural Network always produces the same outputs?

I used MatConvNet to build a CNN model for regression. The input size is 20×20×1×32, the output size is 4×1×32, the convolutional filter size is 3×3×1. Now I found after training the training error ...
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5 views

Correct definition of classes in classification tasks

I have to predict the values of a continuous target variable $Y$ using a bunch of $X$ features. Unfortunately, the regression approch does not provide satisfying results. Thus, I was thinking to ...
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6 views

Suggest suitable time series like ML model

We have data (1901 to 2002) in this schema: Fotrnight, Temp, Precipitation, WetDayFreq, (and other env variables), Cholera_cases we have one such table for each ...
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11 views

Is cross entropy only applied on the last layer of an ANN?

I was reading about L1 regularization and from what I understand, we compute the cost of the last layer like: $\ w_{new} = w−\eta\frac{\partial C_{0}}{∂w}−\frac{\eta\lambda}{n}w $ However when using ...
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6 views

Compute all paths in graph that has multiple inputs and one output

I want to compute all the paths in directed acyclic graph from multiple inputs (x1, .., xn) to one output. The graph has the same depth which d and the inputs come to the graph at the same time (the ...
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106 views

HMMs with feature vectors (block HMMs?)

I'm quite new to HMMs, but still couldn't find an approach to fit HMMs in R, where we have feature vectors instead of single values. Or perhaps I simply didn't understand some of the proposed ...
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22 views

Bias inputs in an RNN

As far as I'm aware, the bias inputs for a feed forward neural network are typically connected as follows: How are they connected in a recurrent neural network? (My guess is below)
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6 views

Can Naive Bayes be used with feature hashing and one hot encoding?

I was wondering if a naive bayes implementation can be used with data that has been feature hashes and one hot encoded. The data I am looking at is mobile ad click data from the avazu kaggle ...
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Classify streaming, partially complete data into groups defined by prior clustering

Suppose I have M observation vectors, offline, $y_t$, $ t =1 ... M$, and each observation is $n$ dimensional. I then cluster these observations into $k$ clusters. For computing the clustering ...
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verified procedure for calculating gradient descent?

I'm wondering if there is a good procedure people are using for gradient descent that is pretty well validated--something like a package for R or Python, or generic code many people adapt. After ...
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23 views

A good description of the random forests method

Can anyone suggest a good book or article describing the random forests method of classification? I'm not satisfied with the way the subject is treated in "An Introduction to Statistical Learning with ...
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SVM - high number of support vectors leads to a high variance SVM?

I've tuned a SVM with radial kernel that has training error about 10%, but test error is about 38%, which surprise me. I tried to understand what may cause this and noticed the number of support ...
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1answer
18 views

Heteroscedasticity in machine learning predictions

I am using a machine learning method (PLS) to predict a continuous variable, which currently does a pretty good job, with reasonable RMSE etc. However, the residuals exhibit heteroscedasticity, where ...
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1answer
10 views

Prediction for non-negative data using PLS/alternative

I am currently using PLS (the set of predictors are quite highly-dimensional) to predict a particular variable, $age$, and I am using Caret's train implementation using the pls method: ...
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5 views

How ksvd algorithm is considered generalized kmean?

I am trying to understand more details of this paper "KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation" proposed a new algorithm called Ksvd and claims it's a ...
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384 views

What are some good datasets to learn basic machine learning algorithms and why?

I am new to machine learning and looking for some datasets through which i can compare and contrasts the differences between different machine learning algorithms (Decision Trees, Boosting, SVM and ...
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Advice on a model or approach to layered dataset

I am attempting to develop a model to estimate the number of people in a space based on the Wi-Fi Traffic. At present, I have a dataset (in xml) which is structured like the following: ...
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Data complexity measure which affect classifier performance

As we strive to explain accuracy of machine learning algorithms, many authors suggest to start by degree of complexity in data. I am working in data complexity measure like: class ...
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5answers
12k views

Using deep learning for time series prediction

I'm new in area of deep learning and for me first step was to read interesting articles from deeplearning.net site. In papers about deep learning, Hinton and others mostly talk about applying it to ...
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917 views

Covariance matrix for Gaussian Process and Wishart distribution

I'm reading through this paper on Generalised Wishart Processes (GWP). The paper calculates the covariances between different random variables (following Gaussian Process) using squared exponential ...
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1answer
515 views

Feature scaling and mean normalization

I'm taking Andrew Ng's machine learning course and was unable to get the answer to this question correct after several attempts. Kindly help solve this, though I've passed through the level. ...
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13 views

Improving my knn prediction

I am still somewhat new to R and machine learning. I am trying to predict the cases of diabetes in the PimaIndiansDiabetes data set. My results were not the best- and I am wondering what I could do to ...
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11k views

Why is Euclidean distance not a good metric in high dimensions?

I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high ...
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13 views

Problem on Clustering Discrete Input using GMM

I want to do clustering using Gaussian Mixture modeling (GMM) on a set of data which is a 5-dimension vector of real values $(x_1,x_2,x_3,x_4,x_5)$. However the clustering result were pretty bad, ...
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227 views

Evaluating features and similarity measures

I am currently developing a classifier, which is supposed to classify into a number of classes. For this purpose I am designing some features and similarity measures which I might use for a later ...
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294 views

Question regarding parameters and variable selection in Mahout algorithm for logistic regression

Below is the list of parameters in Mahout logistic regression. What does "passes" mean? In detail please --passes passes the number of times to pass over the input data ...
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157 views

Using LDA in non-realtime twitter data

I'm trying to understand user characterization from twitter data. How can I infer a user's interests from their status updates? LDA (Latent Dirichlet Allocation) seems to be a suitable approach to ...
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Can you recommend a book to read before Elements of Statistical Learning?

Based on this post, http://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets, I want to digest Elements of Statistical Learning. ...
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Flag Random or Nonsensical Entries in a data set using R

This is a variation on my question Machine learning to catch fraud I have a data set with about 5 million rows The data set contains names and addresses of companies The name of the company is free ...
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17 views

what is “Minimum Length Least Square”

I am in the process of implementing Bayesian Lasso with Normal-Gamma prior; In section 3.3 mention The prior for the scale parameter $\gamma$ conditional on $\lambda$ is given by $v_\beta = 2 \lambda ...
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What neural network architecture and weights can approximate the following functions?

I learned how to represent all the boolean functions such as AND, OR, XOR etc. If a multilayer (recurrent) neural network can approximate any function arbitrarily close, what kind of architecture and ...
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42 views

cross-validation: what is the standard deviation if the same value is obtained for each fold?

Here is a detailed imaginary example: I am using 5-fold cross-validation to estimate the generalization MSE of my predictive model. When I hold-out fold number 1, which contains 10 observations, say ...
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1answer
115 views

Random Forest: Different accuracy, TPR and FPR between training set and test set?

I'm a newbie learning Random Forest. When I use this method to predict my outcome and check with the same data set (training set), I see that the model fits almost perfectly the data. But when I ...
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721 views

Multidimensional scaling using Python

I have 6,000 points for which I have all pairwise distances in a distance matrix. I want to get an idea whether these data were generated by a mixture of Gaussian distributions so I'm trying to get a ...
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1answer
38 views

Classification problem where one attribute is a vector

Hello I am a layman trying to analyze game data from League of Legends, specifically looking at predicting the win rate for a given champion given an item build. Outline A player can own up to 6 ...
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23 views

Cost function spiking upon using dropout on neural network

Upon using the dropout technique, my cost function is spiking arbitrarily. Is this normal? If not, how do I avoid it? I'm using a salt-and-pepper mask to drop out neurons at a dropout rate of 5%. ...
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28 views

Lagrangian multiplier: role of the constraint sign

I am beginner learning Lagrange multipliers with wiki article. Consider: maximize $f(x,y)$ subject to $g(x,y) = 0$ I understand that to maximize I must follow the gradient $\nabla {_{x, y}}^{}f$. I ...
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21 views

How do I use MatLab or R data model in an machine learning application [on hold]

I want to use matlab or R to create a prediction model, once this is complete how would I use that model in a an application?
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Is there a classification model with possibility to set transition probabilities for each sample?

There are models where we can to set initial transition probabilities between classes like Markov models. But what if we need to change the probabilities dynamically depends on all previous ...
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16 views

Preprocessing via PCA in Caret, then fitting PLS

I am dealing with quite highly-dimensional data, and am using (in R) Caret's preprocessing 'pca' method to reduce the dimensionality. However, dependent on the number of components I choose, I seem to ...
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383 views

Weighted covariance matrix using kernels

I would like to create a weighted covariance matrix (say 5 variables) using 3 different time points where the weights come from a kernel function (can be normal, triangular, etc.) but I'm not ...