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libsvm grid search on Parallel Computing Toolbox Matlab

I am using the following code for grid search on libsvm: ...
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36 views

Gaussian Process , selecting the hyperparameters

I am using Gaussian Process regression toolbox from the site http://www.gaussianprocess.org/gpml/code/matlab/doc/ I was able to use implement the code in matlab easily, following the guide lines. ...
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1answer
29 views

Grid Search for hyperparameter and feature selection

So I need to select my hyperparameters and also my features. A full grid search of the space of hyperparameters and features is too computationally intensive, so what I am doing instead is for each ...
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0answers
23 views

How to determine appropriate number of features and also which features to select?

So I have a dataset which I am using K fold cross validation on to select the number of features and which features should be selected. As I understand it, I would set the number of features to be ...
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0answers
13 views

How to get more continuity in regression forest output

I am using a regression forest. What I have noticed when I plot the quantile distribution of the forest's output is that over a long stretch of quantiles (e.g. $\tau \in [0.1,0.3]$), the output will ...
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2answers
71 views

How to select penalty parameter after cross validation?

Say I have a feature matrix $X$ and a target $y$. I use $k$-fold cross validation to generate $k$ out-of-sample MSE curves as a function of a penalty parameter $\lambda$ $$MSE_i(\lambda) \quad ...
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0answers
30 views

Prior elicitation with Normal-Gamma or Normal-Inverse-Gamma

I am looking for a way to have experts elicit a prior for a Normal-Inverse-Gamma Bayesian linear regression model. Is there any material suggesting intuitive ways to go about this?
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93 views

Maximizing incomplete likelihood

Given the conditional distribution $p(x|y)$ and the prior of the hidden variables $p(y|\theta)$ with unknown hyper-parameter $\theta$. Now we have observed i.i.d. samples of $x$. Besides the Bayes ...
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1answer
97 views

Relationship between the kernel and the value of C in SVM's

How exactly does the value of C relate across different kernels that we can use for SVM's? As in, how does it vary when changing the polynomial degree of a kernel or while using a Gaussian kernel?
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1answer
250 views

Hyperprior density for hierarchical Gamma-Poisson model

In a hierarchical model of data $y$ where $$y \sim \textrm{Poisson}(\lambda)$$ $$\lambda \sim \textrm{Gamma}(\alpha, \beta)$$ it appears to be typical in practice to chose values ($\alpha, \beta)$ ...
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0answers
82 views

Fully Bayesian hyper-parameter selection in GPML

Is it possible to perform an approximated fully Bayesian (1) selection of hyper-parameters (e.g. covariance scale) with the GPML code, instead of maximizing the marginal likelihood (2) ? I think using ...
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0answers
102 views

Choosing an appropriate minibatch size for stochastic gradient descent (SGD)

Is there any literature that examines the choice of minibatch size when performing stochastic gradient descent? In my experience, it seems to be an empirical choice, usually found via ...
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36 views

Intuition on how to adjust the gamma and lambda hyper-parameters in reinforcement learning

In any of the standard RL algorithms that use generalized temporal differencing (e.g. SARSA, Q-learning), the question arises as to what values to use for the lambda and gamma hyper-parameters for a ...
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1answer
176 views

Parameter learning of Markov random field

Given a Markov random field $\mathcal{G} = (\mathcal{V},\mathcal{E})$, the corresponding density function to which is expressed by $f(x) \propto \prod_{x\in\mathcal{V}} \psi_u(x) ...
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0answers
69 views

Rule of thumb for tuning the values of the penalty parameter in SVM models

I have recently been running into computational issues in fitting a soft-margin SVM model using the e1071 package in R. The issue is unavoidable since the problem ...
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1answer
2k views

About SVM cost and gamma parameters tuning

I am using R and e1071 package to tune a C-classification SVM. My question is: regardless of the kernel type (linear, ...
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2answers
85 views

Random search for the optimal number of input features and optimal number of hidden layers for a MLP?

I've performed a random search in hypothesis space $$\{(c,h)| c \in U[1,256]; h\in U[1,100];c \in \mathrm{Z} \text{ and } h \in \mathrm{Z}\}$$ that defines the parameters of a standard multilayer ...
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1answer
154 views

Selecting optimal number of input features and optimal number of hidden layers for a MLP?

What is the best way to select parameters for a binary neural network classifier? More specifically I have 265 features ranked according to Mutual Information Criterion. I have to determine the ...
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1answer
375 views

How to optimize hyper-parameters in LDA?

After reading Hanna Wallach's paper Rethinking LDA: Why Priors Matter, I want to add hyper-parameter optimization to my own implementation of LDA. However, the paper doesn't given any details about ...
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1answer
348 views

Estimating hyperparameter in basis functions (Gaussian and sigmoid) for linear regression

I am working on a linear regression problem with Gaussian and sigmoid basis functions. My data set is very large, say a total of 15K inputs with each input having 46 features. I have divided my data ...
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2answers
2k views

Natural interpretation for LDA hyperparameters

Can somebody explain what is the natural interpretation for LDA hyperparameters? ALPHA and BETA are parameters of Dirichlet ...
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1answer
754 views

Trouble minimizing perplexity in LDA

I am running LDA from Mark Steyver's MATLAB Topic Modelling toolkit on a few Apache Java open source projects. I have taken care of stop word removal (for e.g. words such Apache, java keywords are ...
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0answers
80 views

How to use multiple datasets in order to measure the performance of a learning system?

I’m working on a project where I need to test a machine learning system which has a lot of hyper-parameters. Further, in order to gauge the performance of system, I’m planning to use several ...
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1answer
166 views

Hyper-parameter optimization via random search

I’m working on a classification system which consists of an auto-encoder for feature learning and logistic regression for classification. The system has five hyper-parameters as enumerated below. ...
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2answers
196 views

Bias in classifier model selection

Say I have a set of classifier models, each generated using feature selection inside a repeated k-fold cross-validation. Each classifier model is generated using a different set of regularization ...
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1answer
1k views

Hyperparameter estimation in Gaussian process

I am trying to optimize the hyperparameters for a Gaussian process. As a starter I choose the squared exponential function for covariance where iI have to optimize 3 parameters $\sigma_f$, $\sigma_n$ ...