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

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Does the expectation maximization algorithm apply to this problem?

I have a sample of variables $x_i$, where each one is a function of known variables $y_i$ and $b_i$, and of an unknown variable $\alpha_i$. $$x_i=y_ib_i-y_i^{\alpha_i}$$ The density function of ...
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14 views

How z-score normalization is best strategy for data normalization in classification task

I have read in many technical articles that z-score normalization is best way of normalizing data in classification task. But, it subtracts mean of data from every sample in data. So, data of two ...
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28 views

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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9 views

automatic background removal from images without user interaction

I am trying to develop an image search application. I have crawled through e-commerce websites and obtained some data set of images(~2.5 Million). Now I want to identify the object of interest from ...
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training/learning a function from domain and range alone?

Usually in supervised learning we have a data set $D$, which informs you for a certain input $x_i$ we expect a certain corresponding output $y_i$, and the appropriate function $f$ is trained by ...
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13 views

Project management use case - how to predict resource allocation

I have a historical data set with project information including things like duration of project, number of resources per week, number of hours worked per resource per week. I was wondering if there's ...
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30 views

Given yelp data, how to find top 100 restaurants in America?

Every year, Yelp released top 100 places to eat in America based on their data. I'm wondering how to design a machine learning algorithm to do this? This is definitely an open question. My basic idea ...
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17 views

help in coding decision tree in python [migrated]

I am not sure if this is the right place to post this, but I have been trying to code up a simple decision tree class for a while and am getting lost at various points. Specifically, I'm not sure ...
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61 views

Understanding k-means unsupervised learning for features

I'm following this paper: http://ai.stanford.edu/~ang/papers/icdar01-TextRecognitionUnsupervisedFeatureLearning.pdf And I'm trying to understand specifically how the k-means approach works when ...
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2answers
56 views

How can I force my model to predict the samples that are close to zero?

I have a large amount of inventory data and I am trying to predict when the inventory gets low using one component of the change in inventory (yes I know this doesn't describe inventory very well by ...
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1answer
54 views

Will Linear Regression choose as good of a model as any other regression algorithm given enough data?

I am in the seemingly unusual situation of having practically unlimited data. In this case, will linear regression choose as good of a model as any other algorithm in the case where the number of ...
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49 views

Understanding decision trees

Im new to decision trees and am trying some decision tree modelling now with titanic data. I have the following dataset. ...
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33 views

Model based approaches to content based recommenders. How does this work?

I have a question regarding the use of model based approaches to recommender systems. So, the goal is to create a model that predicts the user reaction to a specific item. Either a rating scale or ...
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1answer
28 views

Methods to reduce the number of class labels

I have a dataset with 17 classes which will be predicted using a classification algorithm (QDA, Decision Tree or something similar) using between 2-4 features. Many of these classes have significant ...
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57 views

How to deal with imbalance data in , for example, neural network

Do we usually discard this issue, just train the neural network then compute the AUC, or can we use weighted version of loss function, for example, in binary classification problem, can we use ...
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1answer
36 views

How to optimize the number of topics using R Mallet

I would like to select the optimal number of LDA topics using R's mallet library. I know that there are several ways to do this using other implementations of LDA ...
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1answer
28 views

What statistical test should I run to select “explicative” features in my dataset?

I have a database with more than 500 samples with 22 quantitative features each and I would like to predict a categorical variable (0 or 1). I am trying to fit a logistic regression model and a neural ...
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1answer
25 views

Factored sparse coding: how to differentiate coordinate transform?

I'm trying to implement a sparse coding technique described by Bergstra et al in this paper. The encoding process there includes the following computation: $translate(rotate(scale(\mathbf{u}, \alpha, ...
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2answers
86 views

From where I can learn to use scikit learn

I have read text about machine learning and I feel that I have gained sufficient knowledge that I can start applying them practically. I have programming experience in python so I want to learn how to ...
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33 views

Choosing contrast coding in R

I am working on a data set with categorical variables. To apply ANN, I want to apply contrast coding to those variables. But how do I choose between coding functions in R (sum, helmert, treatment ...
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18 views

Statistical/ML models when observations have different amounts of input

Let's say we're predicting an employee's performance review score for the following year based on his/her performance review scores from each previous year of their employment. We might have these ...
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15 views

pattern recognition in spatial data in R

I am currently working on telemetry data for the wild animals. I have extracted their paths over the map. I have also calculated the angular movement per activity. In my dataset I am given with the ...
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18 views

K means algorithm on an image

Hi I want apply my k means algorithm on an image to a certain number of k clusters.I want to ask is that How is the algorithm applied ? I mean what i have learned from internet is that k means is ...
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7 views

MLP with hidden layer units more than input layer

What is an acceptable scenario in multi-layer perceptron when the number of neurons in the hidden layer is more than input layer neurons. What changes to the cost function should be made for such a ...
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1answer
19 views

A priori parameters to Random Forest based on n and p

If cross validation is too costly to determine the number of trees and number of max_features is there any standard to what you choose based on n and p. I know sqrt(p) is standard for max_features but ...
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2answers
63 views

High accuracy during cross validation, low accuracy on test set

I'm currently trying to build a tennis prediction model. Unfortunately, I have some issues that I hope you could help me to handle. I have 1110 examples of matches from the year 2013, with their ...
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3answers
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How to build a predictive model that uses only a subset of training factors , for testing?

Generally, all the predictive algorithms work as follows : input factors : x1,x2,x3,x4...xn<br> responses: y1,y2..ym <br> and the model (say M) built ...
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42 views

PCA to reduce dimensionality then classifier of choice?

I read about PCA online and the way it computes a covariance matrix, computes eigenvalues, and then transforms the matrix to reduce the dimensionality of the matrix to a certain number k <= p. ...
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16 views

Incorporating letter transition model into linear-chain CRF

Suppose I have a linear-chain CRF for e.g. handwriting recognition, $$ p(\mathbf{y}\mid\mathbf{X}) = \frac{1}{Z_\mathbf{X}}\exp\left(\sum_{j=1}^m\mathbf{w}_{y_j}^T\mathbf{x}_j + ...
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1answer
47 views

K-fold repeated cross validation for classification accuracy in Caret

I am new to cross-validation and I have a data-set called LDA.scores for 12 measured call-type parameters. I am trying to run a k-fold repeated cross validation with 10 folds and associated naive ...
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21 views

Stochastic Gradient Descent vs Online Gradient Descent

I was wondering what the difference between stochastic gradient descent and online gradient descent is? Or is it the same algorithm? Thanks.
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35 views

receiver operating characteristic (ROC) on a test set

The following image definitely makes sense to me. Say you have a few trained binary classifiers A, B (B not much better than random guessing etc. ...) and a test set composed of n test samples to ...
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1answer
29 views

Imputation and linear regression analysis paradox

Missing values, especially in small datasets, can introduce biases into your model. There are several data imputation methods (MICE, Amelia II), which use EM algorithms to "fill in" the missing ...
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20 views

How effective is randomly/algorithmically generating exorbitant amounts of training data for a neural network?

There are some problems of which the generating of data is easy whereas the inverse is not. For example, use a 3D game engine to render some randomly generated objects with some random changes and ...
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10-fold cross validation for forecasting time series with explanatory data ?

I saw that the question was asked some years ago here, but I wasn't satisfied with the answers so I'm asking it again. Is there some theoretical foundations about not doing k-fold cross validation ...
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20 views

What is correct implementation of LDA (Linear Discriminant Analysis)? [migrated]

I found that the result of LDA in OpenCV is different from other libraries. For example, the input data was ...
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1answer
23 views

How to use external data?

I'm building the model using the internal data to predict the health situation of the customer. I've just found about 100 new "external data" in the form of region data. Based on this new information, ...
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13 views

On which kind of problems preprocessing data using RBMs (unsupervised) could give an edge?

I am new to machine learning and basically so far I've been using only supervised algorithms, however, recently I started to use RBMs in combination with some classifier (using a pipeline in scikit ...
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2answers
42 views

In back propagation for neural networks, what exactly is the “error signal”?

For example: Imagine we end up with a sum of 0.755 on our output node. We then apply the activation function (in this case I'll use a sigmoid) which gives us a ...
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21 views

What value of (adjusted) McFadden R square or other pesudo R square means good fitting

I got adjusted MaFadden R square for logistic regression: 0.918772 , 0.6135568 , 0.3407252 respectively, which value is good? I just heard the value between 0.2 and 0.4 is good for McFadden R square. ...
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14 views

Which model should I use for extracting binary features?

I create some data, a normal matrix: 1, 11, 0, 20 ..... 1 1, 0, 0, 11..... 1 1, 5, 0, 22..... 0 each row include more than 2000 integer. they are following some patterns. The requirement is ...
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32 views

What is the difference between data normalization and feature extraction in deep learning?

I am learning about the multi-modal deep learning models and the papers I am reading are very confusing on one point in the process: "feature extraction" and "data normalization" seem to be used ...
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59 views

How to build “supervised clustering” for neural networks?

I'm confused as to what the output would be. Consider the "blind source separation" problem. Let's say I have a ton of training examples where the input is the final cacophony of sounds as a sound ...
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1answer
21 views

Is multiple stage binary classification a good idea if you have very few positives?

The problem is the following: We have a set of, say 5000 documents, with a single binary label. Say that 4900 documents are negative and only 100 are positive. I built a binary classifier while ...
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24 views

Any idea about application of deep dream? [migrated]

Recently Google publicized interesting deep dream. Besides art generation such as http://deepdreamgenerator.com/, do you see any potential applications of deep dream in computer vision or machine ...
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21 views

How to handle many-to-one data?

I'm trying a kaggle contest, just to improve my machine learning skills. The challenge I currently do involves many-to-one relational data. For instance, a person belongs to a municipality. A ...
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2answers
54 views

sklearn - overfitting problem

I'm looking for recommendations as to the best way forward for my current machine learning problem The outline of the problem and what I've done is as follows: I have 900+ trials of EEG data, where ...
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1answer
35 views

Kernel PCA for feature selection for various machine learning algorithms [duplicate]

I would like to forecast stock index returns with SVM, k-NN, and Neural Networks. In advance I want to select my inputs via kernel PCA (kPCA). Everything is performed in R. For the KPCA I use ...
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29 views

MNIST dataset black or white background

In the MNIST dataset, are the images on white or black background? I seem to have encounter both type of images by googling around. Does the background color has any effects on the performance of a ...
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1answer
36 views

How to apply Cross Entropy on Rectified Linear Units

I am currently getting started with Machine Learning. However, I have some problem to derive formula and not able understand how to applied the Cross Entropy (CE) on Rectified Linear Units (ReLU). I ...