Tagged Questions

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

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0
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0answers
37 views

Prediction for new data using trained Gaussian Mixture Model

I am not sure how to do the prediction for some new data using trained Gaussian Mixture Model (GMM). For example, I have got some labelled data drawn from 3 different classes (clusters). For each ...
2
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0answers
19 views

prediction of variable in the future?

I have some data from sensors in my phone.I have their respective battery levels at each timestamp the sensor readings were recorded in phone.My aim is to be able to predict, lets say that i have ...
0
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3answers
44 views

Is kNN best for classification?

I wanted to know if kNN might produce the best result for classification? Since, it is not model based, it does not loose any detail and compares every training sample to give the prediction. Hence ...
-1
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0answers
41 views

machine learning versus statistical technique [duplicate]

Is there any difference between machine learning and statistical techniques.If there is any difference please provide any research paper which justified it..
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2answers
96 views

What are the differences between sparse coding and autoencoder?

Sparse coding is defined as learning an over-complete set of basis vectors to represent input vectors (<-- why do we want this) . What are the differences between sparse coding and autoencoder? ...
0
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1answer
17 views

Would it make sense in batch learning to swap only part of the batch in every iteration

The context of this question is a simulated annealing process, but I would be also interested to know the effects on neural nets and other learning methods. Say I have a dataset of 100k instances, ...
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0answers
39 views

Difference between tuneRF {randomForest} OOB error and Model OOB error

I have used tuneRF {randomForest} function to know best mtry and got OOB error is almost 19%, however when i run the model using randomForest am gettin OOB error 28%. I am running the model on same ...
3
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2answers
56 views

Classifying by performing PCA for positive and negative datasets separately

I have a dataset with binary labels, and I try to figure out whether the data can be classified and yield the ground-truth labels. I thought to try PCA for the data with each of the labels, and see ...
0
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0answers
18 views

SQL query optimization using machine learning

This is related to my thesis work. I am trying to use SVM for query optimization. After finding the best query plan, I have to train the machine so that whenever the same type of query appears the ...
1
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1answer
30 views

Data normalization in k-means and svm

Generally if I want to normalize my data in which direction I should normalize (subtracting mean and dividing by std)? Lets say I have a data matrix D (...
2
votes
2answers
67 views

Why does the scaling of feature vectors improve performance of SVM classifier?

I've found that performing scaling in SVM problems really improves the performance of SVM ... But I don't understand why! I have read this explanation: "The main advantage of scaling is to avoid ...
0
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1answer
20 views

Why is it necessary to assume that examples and labels are drawn from a joint distribution in empirical loss minimization?

Multiple sources have indicated that when trying to minimize empirical loss, $1/N \sum_i L(f(x_i, w), y_i)$, where $L$ is some loss function, $y_i$ is the true label, and $f(x_i, w)$ is the predicted ...
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0answers
7 views

Hand Coordinates Clustering for vector quantization

I've a sequence of pitch, yaw, roll of the hand, plus pitch and yaw of the fingers. So i got a 13-dimensional vector. Which is the best way to understand how to cluster these data in order to perform ...
1
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0answers
31 views

How can I make sure that an LDA implementation works?

I am currently experimenting with neural nets for classification of on-line handwritten data (hence: not pixels, but time series data). To do so, I use several toolkits (internal development of my ...
2
votes
1answer
54 views

Can someone explain to me the Bayesian classification model?

I often read about converting from a normal classification model like logistic regression and then using an equivalent Bayesian model. As I understood, it's somehow the same model but with a different ...
0
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0answers
9 views

Matching an element from a set of abstracts to an element in set of titles

Suppose I have two sets, a = {"this is a title", ...} b = {"this is a short description of some title from a", ...} What is the best way to find the best match ...
0
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1answer
15 views

What are the differences between 'epoch', 'batch', and 'minibatch'?

As far as I know, when adopting Stochastic Gradient Descent as learning algorithm, someone use 'epoch' for full dataset, and 'batch' for data used in a single update step, while another use 'batch' ...
0
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0answers
17 views

How can I improve my sklearn logistic regression model

My objective is to classify sentences into useful (denote in boolean as 1) and not useful (denote in boolean as 0) categories. I have about 525 features where 300 features are the most frequent and ...
0
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0answers
39 views

Confidence interval for precision and recall in classification

I have implemented a classifier and I can calculate the precision and recall when testing the classifier (two classes) in the test set(>100). Is it meaningful to talk about the confidence interval for ...
0
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0answers
29 views

How to compute/run LDA with 3 classes

I couldn't find one example on how to compute LDA with 3 classes (nor what is the algorithm). for example i have the following observations and classes: (each observation in one-dimensional) $ ...
0
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0answers
24 views

What is the use of convolution and pooling in convolutional neural network

I am confused the use of convolution and pooling in convolutional neural network(CNN). I know pooling is basically summarizing the adjacent features, which can greatly reduced the features size of an ...
1
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0answers
39 views

Performance of algorithms using Jacobian matrix on large data sets

Some ANN learning algorithms like Gauss-Newton, Levenberg-Marquardt require creation of a Jacobian matrix. Having studied this I found out that the Jacobian matrix is huge (unless I misunderstood ...
0
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0answers
21 views

Least-squares fitting with only optimum features, after Lasso - valid?

Using Lasso reduces the coefficients of features of a model, reducing some to zero, and thereby performing feature selection. The number of features depends on the value of $\alpha$ aka $\lambda$. In ...
0
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0answers
14 views

Is there “infinite” universal model selection ? and Structural Risk Minimization

I ask these because I come up with an idea : If I have infinite and universal model set, then there must exist model that totally fits my data and no parameter for the model so the complexity ...
0
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1answer
45 views

Appropriate non-parametric post-hoc test for baseline comparisons?

I want to evaluate several "classifiers" (machine-learning algorithms) with paired samples. I do not want to compare each algorithms' performance to every other (n x m comparison) but only compare the ...
0
votes
1answer
32 views

what does the numbers in the classification report of sklearn mean?

I have below an example i pulled from sklearn 's sklearn.metrics.classification_report documentation. What i dont understand is why there are f1-score, precision and recall values for each class ...
2
votes
3answers
111 views

Good sources to get papers in machine learning?

I'm a CS master student (will be a PhD in three months or so). Today I was at my superviser's office and he had a friend discussing some ideas for their research. Then they mentioned a paper that I ...
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0answers
29 views

how to superimpose two matlab images rigidly transformed to perform a metrics

i got two MRI images on matlab, i need to perform an intensity based registration similarity metrics in order to get a registration. The problem is that, since i got some rigid transformation on one ...
1
vote
1answer
17 views

Weight Decay in Neural Neural Networks Weight Update and Convergence

I have a neural network (That I created using java) for a class assignment that is working when I do not use any weight decay value, but when I use a value greater than or equal to .001, my accuracy ...
0
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0answers
36 views

Using third validation set in Cross Validation?

(Note there's 2 paragraphs of background information before I get to the question) I've got a Neural Network classifier, trained with an EA to classify data. I previously used a holdout framework ...
1
vote
1answer
20 views

Logistic-Regression: Prior correction at test time

Using sklean.linear_model.LogisticRegression for a binary classification problem. My classes are unbalanced. The positive class comprises about 20% of the training set. When fitting the model I use: ...
0
votes
1answer
42 views

Crossover and Mutation in Genetic Algorithm

I am studying how GA works. It is known that GA obtains the optimal solution by iteration through the the process of reproduction, crossover, and mutation. When crossover and mutation, the ...
3
votes
3answers
117 views

Understanding Gaussian Basis function parameters to be used in linear regression

I'd like to apply the Gaussian basis function into a linear regression implementation. Unfortunately I'm having a hard time understanding a couple parameters in the basis function. Specifically mu ...
1
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0answers
29 views

Machine Learning Models For Real Time Sales Data

I am working on a predictive analytics problem related to Sales where based on interaction with a prospect we try to predict whether the deal will close or not. In sales, the data updates whenever a ...
0
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0answers
197 views

In Graph Transformer Networks, which parameters are tuned during back-propagation?

Referring to the milestone paper "Gradient-Based Learning Applied to Document Recognition" of LeCun, Bottou, Bengio and Haffner, which parameters of the graph transformer networks for global training ...
0
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1answer
28 views

A simple question on CLT in possible connection with Berry-Esseen thm

I am curious about the contents while I read a note on machine learning. It could be obvious. So, please let me know if I am missing some fundamental things. $X_1,X_2,...,X_n$ are from an i.i.d. ...
0
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0answers
31 views

Neural Networks sigmoid activation with bias updates

I am trying to figure out if I am creating an artificial neural network using the sigmoid activation function and using bias correctly. I want one bias node to input to all hidden nodes with static ...
3
votes
2answers
188 views

Pattern recognition with time series analysis

I'm looking for some good pointers to pattern recognition with time series. Possibly something practical that can be easily understood. As a toy example, think about collecting data from an ...
0
votes
1answer
27 views

Use of infinity norm instead of SSE for machine learning accuracy?

Are there any examples or arguments in favor of using an infinity norm (or equivalent) over sum of squared errors or root mean squared error for evaluating machine learning algorithms?
0
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0answers
11 views

What is the relationship between separable SVM test error bounds and soft-margin SVM test error bounds?

Can I compute the bounds for a soft margin SVM by taking the VC dimension for an SVM and using the misclassified examples as train error? Does the inequality from wikipedia's VC dimension page hold ...
1
vote
1answer
72 views

Strategy for Analyzing Data

I have been learning about Machine Learning (via Udacity) and Statistics (via Coursera) the past few months and trying to figure out a good way to combine them for a general approach to explaining ...
0
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0answers
34 views

What is the next step after acquiring the parameters(means, covar, priors) from GMM via EM

I am comparing the results achieved from clustering via K-means and GMM. For comparison I have accumulated a dataset of images. The training set consists of 359 images. I used SIFT to extract the ...
0
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0answers
28 views

How to calculate multicollinearity of binary variable with other predictors in regression model?

VIF can be used to calculate multicollinearity of continuous variable in regression models. But VIF will only work for continuous variables because this is calculated by running a linear regression ...
1
vote
0answers
45 views

Dichotomizing Continuous Variables in Regression: Good or Bad? [duplicate]

I believe Dichotomizing(also called bucketing/binning) of continuous variable is not always a good idea. My colleague while building regression model always bins continuous variables and only keep ...
0
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0answers
21 views

Intuition on One Class Support Vector Machines

I am having trouble understanding how one-class SVMs work. They were introduced in a paper by Scholkopf and others (and can be found here). One-class SVMs perform "novelty detection", where a point ...
0
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0answers
42 views

What is a convolutional neural network

I have been studying neural networks and I recently found out about deep learning and convolutional neural networks. Can someone give me a newbie introduction to convolutional neural networks, what ...
0
votes
1answer
19 views

Affinity propagation comments

I was looking into affinity propagation for my similarity matrix problem and thought it would fit well. However, browsing literature I found this comment that basically breaks both legs of affinity ...
1
vote
2answers
36 views

Classes distribution in training set

In my actual data class A has 90%, class B has 9% and class C has 1% (numbers are made up for sake of simplicity). Now I want to prepare a training set for my classifier (I plan to use Vowpal Wabbit). ...
0
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0answers
18 views

Use cases for P-Kernel for SVMs

I've been reading the book by Cristianini on Kernels (2004) where generative kernels (like p-kernel and fisher-kernel, not to be confused with polynomial kernel!) are described. I am interested in ...
0
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0answers
20 views

Comparison of supervised learning algorithms with different data types

I've been looking for review type papers of supervised learning techniques that focus on the type of data being used to train e.g. factors with many levels, binary factors, continuous variables etc. I ...