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Questions tagged [structured-prediction]

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Example of a problem with structured output labels

I'm studying SSVM (Structured SVMs). On my book is stated that Structured SVM is an extension of the SVM, in which Each sample is assigned to a structured output label z ∈ K, e.g. partitions, ...
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Regression - many samples have the same target

I have a machine learning problem in which I have a many-to-one relationship from samples to targets. I have ~3k samples but only 11 targets with a shared key YEAR ...
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Quick search based on similarity: logarithmic time

I have some objects $x\in X$ and a metric $s:X\times X\to\mathbb{R_{+}}$. For each $x$, there is a $y\in Y$. Note that $x$ and $y$ are highly structured and we cannot consider neural networks for ...
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Conditional Random Field for Hyperspectral Image Classification

How can I separate training and test sets in a hyperspectral image and apply Conditional Random Field (CRF) for pixel classification? If I choose pixels randomly, some of the neighboring pixels of a ...
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How to take advantage of the information/structure we have in the labels in multiple output regression?

I have a regression problem where each observation possesses a vector of features and 4 associated responses. These responses, as in many problems are correlated. It would be nice to be able to ...
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doubt regarding structural correspondence learning

I was going through the paper titled "Domain Adaptation with Structural Correspondence Learning" (http://john.blitzer.com/papers/emnlp06.pdf). I am writing this to resolve one doubt that I ran into. ...
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How to encode dependence structure in regression (Bayesian networks)

Suppose you know the model should be of the form y =f(x1)g(x2,x3), where f and g are the functions I'm trying to find. Essentially, x2, x3 collectively predict some hidden variable z, and the response ...
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Options for spatially correlated outputs (i.e. structured regression)?

I have a convolutional neural network which takes a 40x40 real valued input, and maps it to a real valued 40x40 output. I've optimized the number of convolution layers, filter size, hidden layer size, ...
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predicting tree structure

This topic is actually rather hard to google for as 'tree' has been overloaded in this domain to refer to decision trees. I'd be interested in having a learning algorithm produce code, such as used ...
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Deep Learning vs Structured Learning

I am interested in the differences between using large, deep learning networks vs Probabilistic graphical models (PGMs), like Random Field models, for structured learning (e.g. on images, or labels of ...
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Which model for this information extraction problem?

I am trying to solve the following pattern recognition / information extraction problem. Assume I have a text where each token has been annotated by a single class among $K$ classes available (with a ...
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Linear CRF is to LR what _____ is to RF/SVM

This figure from (1) shows the relation between LR and linear CRF: linear CRF is pretty much the generalization of logistic regression to sequences: Is there any established generalization of random ...
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Binary linear SVM in terms of structured SVM

On slide 32/85 of this tutorial on structured SVM learning, the author formulates binary SVM classification in terms of structured output SVM by defining $\Psi(x, y) = \frac{y}{2}x$. Why is there a ...