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0
votes
0answers
5 views

Honglak Lee MNIST CDBN filter shapes?

This question is based on Honglak Lee's paper "Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations". In chapter "4.3 Handwritten digit ...
0
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0answers
13 views

Importance of class frequency in classification

Suppose we are classifying instances into n classes that, in practice, occur at frequencies p1, p2, ... pn, (for example classifying news-articles as one of n different topics). For the purposes of ...
0
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1answer
45 views

Once you have a set of training data, how do you go about training a model?

I've been trying to teach myself machine learning using some books, kaggle and other resources so I'm still very new at this. I'm trying to do a competition on kaggle and I've obtained a dataset. ...
0
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0answers
16 views

How to choose negative training sample for Classification problem

Choosing positives sample is a relative straightforward task, but I'm having some problem on determine what should I use for the negative example. I'm working on a SVM binary classificator, trying to ...
1
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0answers
54 views

Pre-training deep neural networks by supervised learning

When pre-training deep neural networks layer by layer, is it normal to pre-train the layers -which haven't been pre-trained by unsupervised training- by using supervised training before we train the ...
0
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0answers
26 views

semisupervised classification training on all or part of unlabeled data

I have 3 sets of data. A positively labeled dataset. An unlabeled dataset that has for sure positive (around 75%) and negative data. An unlabeled dataset that has for sure positive data and maybe ...
0
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0answers
26 views

Minimizing the Training data

I have a grey-box model of the form Y= a + b X1 + c X2. Where a, b and c are the coefficients based on regression. The regression variables X1 and X2 are determined based on ...
1
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0answers
148 views

Tips for training dropout neural network

I use NN for my mini project research, and I found out the newest trick for feed forward NN is using dropout for regularization instead of L1/L2 norm and rectified linear unit as an activation ...
0
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1answer
54 views

How to compute the weights gradients for Convolutional RBM with CD?

I've successfully implemented a RBM and trained it with CD-k, but I now have some issues doing the same with Convolutional RBM. The visible and hidden biases gradients are easy to compute: v1 - v2 ...
0
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1answer
33 views

how to handle (many) false positives in training dataset for logistic regression classifier

I want to train a logistic regression dataset. I have a quite big training data set ( >100 000) and have around 10 features I can train on. Half of my training data is negative training data and I ...
0
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0answers
13 views

Latent semantic classification

How can I create a training data set for document classification using LSA? I have created a term-to-document matrix and have class labels also. I don't know whether to add these class labels in a ...
0
votes
1answer
71 views

How to speed up the training in neural network when mini-batch training is used?

Can anyone give me some ideas on possible techniques to speed up the training process of multilayer artificial neural network if the training involves mini-batch? So far, I understand that stochastic ...
0
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0answers
11 views

Speech recognition, words out of dictionary

I'm performing word recognition by using a tradional procedure. I'm extracting MFCC features. Then I'm creating a code book in order to do vector quantization. After that, I train discrete HMM for two ...
1
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0answers
53 views

HMM library, different length sequences training

I'm using the Kevin Murphy's HMM library in MATLAB(http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html) There is a section called 'How to use the toolbox'. There is this example for GMM ouputs: ...
2
votes
2answers
68 views

Split train//validation/test sets by time, is it correct?

Here's the scenario, slightly altered to a common one. Credit card fraud, payments for the last 12 months (a rolling window). Train with the data from the first 10 months, validate with data from the ...
0
votes
0answers
13 views

Measures to take if activation in neural network gets saturated

I am coding an auto encoder with mnist data. It have 784 inputs, 50 hidden units, 784 outputs. Since outputs are huge in number, when error gets propagated during gradient descent, error of hidden ...
2
votes
2answers
81 views

Building training set from unlabeled data

I want to build classifier using naive bayes. I know that naive bayes is supervised learning that requires labeled data for training set. However, I only have data with no label on it. Is there any ...
0
votes
3answers
204 views

Neural network packages which allow shared weights and parallel training

I'm curious if there are any neural network packages out there that easily allow one to construct feed forward neural networks with shared weights, but also allow for the training to be done in ...
3
votes
1answer
97 views

Imputation before or after splitting into train and test?

I have a data set with N ~ 5000 and about 1/2 missing on at least one important variable. The main analytic method will be Cox proportional hazards. I plan to use multiple imputation. I will also be ...
11
votes
3answers
229 views

Training, testing, validating in a survival analysis problem

I've been browsing various threads here, but I don't think my exact question is answered. I have a dataset of ~50,000 students and their time to dropout. I am going to be performing proportional ...
1
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0answers
163 views

k-means + linear regression: How to split the data for validation

I want to cluster my data first using k-means and then determine a regression model for each cluster. Then I want to evaluate the performance of this approach using split validation. I can think of ...
4
votes
1answer
309 views

How to perform parameters tuning for machine learning?

I have a very basic question regarding parameter tuning using grid search. Typically some machine learning methods have parameters that need to be tuned using grid search. For example, in the ...
0
votes
0answers
48 views

Appropriate bounding boxes on images for training data?

This is the first time I'm doing research in computer vision, and I'm going to be using OpenCV's cascaded training, for which I'm preparing positive samples for training data. If anyone's experienced ...
0
votes
1answer
40 views

Improvement on duplicating instances

I have a task of Relationship extraction. There are some set of predefined relations in the corpus. I need to train classifier to recognize the type of relation or the lack of relation between every ...
1
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0answers
242 views

CV.glmnet results [duplicate]

I was working through the lab on ridge regression and LASSO in ISLR and I came across a strange behavior in the cv.glmnet function. When I followed the lab as ...
1
vote
1answer
824 views

What is the best strategy to train and validate classification using PLS-[classifier] in caret package?

I have 4 clusters (see plot below) extracted from data of medical samples N=218 measured for 11 genes/predictors P=11 by this ...
0
votes
0answers
36 views

positive samples in dataset for classifier training - how should environment figure in?

I have a question about preparing the dataset of positive samples for a cascaded classifier that will be used for object detection. As positive samples, I have been given 3 sets of images: a set of ...
1
vote
0answers
88 views

3D Gaussian basis function

I need tο use 3D data for training and for that I need to find the Gaussian basis function. I know how to find $f(x,y)$ but how can I find $f(x,y,z)$?
1
vote
1answer
101 views

Linear regression from data that don't represent a function

I have $(x,\ y)$ pairs with a strongly suspected linear correlation. So I want to fit the "best" linear function in order to make predictions for unknown $x$'s. These pairs don't represent a function, ...
1
vote
1answer
345 views

Data split into training and test

I am implementing an EEG classifier with 15 subjects (patients), specifically a support vector machine classifier. I randomly choose the training and testing sets, but I was faced by a question "how ...
0
votes
0answers
228 views

Metric warning using caret's rfe

I am using the caret package to do feature selection with rfe while training a knn ...
2
votes
2answers
69 views

Is it always bad to retrain your model to include predicted data?

I understand intuitively why this is a horrible idea - you assume your model is correct and then increase your number of observations which will likely result in a poor fit on future data. I'm ...
1
vote
1answer
426 views

R caret difference between ROC curve and accuracy for classification

In case of caret package test function metric option, one can use either accuracy or ROC as a metric that will be used to finalize values of tuning parameters. I felt that accuracy and ROC are the ...
1
vote
1answer
424 views

LGOCV caret package R

i am learning data mining through book . During classification chapters about Neural Networks the authors have below code. I have below questions: ...
0
votes
2answers
149 views

Text Annotation tools

I have tried open nlp NER for extracting organization names with not great success (It could be model is not fit for the domain I am working). So, I am planning to train Open NLP NER on my training ...
3
votes
1answer
182 views

Feature selection in the training set

I have a classifier, and I am using leave one out cross-validation to assess its performance. On each iteration, I divide the dataset into training and testing sets. The testing set is just the ...
3
votes
1answer
112 views

Methods for time-series prediction depending on multiple parameters

We have hourly time-series data of the status of a system: number of people present at different train stations. We collected it for a year, and we want to use it to train a model to predict the ...
0
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0answers
90 views

Forming training set for Multinomial Naive Bayes

Is it true that Multinomial Naive Bayes requires equally by count training data for each class to get best performance? For example, we forming classifier for three classes - Japan, China, Korea. ...
1
vote
2answers
176 views

Combining labeled and unlabeled data for training

When training my algorithm, if I can get some i.e. data my future test data that has no labels can it improve my algorithm's efficiency, is there any mathematical proof for it? PS: I think ...
3
votes
0answers
336 views

Logistic Regression Cost Function issue in Matlab

I'm trying to implement a logistic regression function in matlab. I calculated the theta values, linear regression cost function is converging and then I use those parameters in logistic regression ...
3
votes
1answer
304 views

SVM retrain on whole dataset for final model --> overfitting?

i am training a SVM (RBF kernel) with a dataset of ~1500 samples (balanced) using fminsearch on the CV error for parameter optimization (C and s). After i found the "best" parameters (local optima ...
0
votes
1answer
199 views

What is Generalization errror on training set. How can I see it on weka?

I get output like this .. ...
1
vote
1answer
580 views

Can a neural network output represent a posterior probability?

I seem to remember from years ago when I first read Bishop's ANN book that it is possible to construct a neural network such that the outputs should represent the posterior probability that I would ...
5
votes
5answers
1k views

Is using the same data for feature selection and cross-validation biased or not?

We have a small dataset (about 250 samples * 100 features) on which we want to build a binary classifier after selecting the best feature subset. Lets say that we partition the data into: Training, ...
1
vote
1answer
195 views

Machine Learning and training time: is it really relevant?

I have a question regarding the time needed for training a classifier. I am facing the specific problem of Sentiment Analysis (classification of text as pos/neg/neu). (Excepting online learning ...