28,134 research outputs found
GPstruct: Bayesian structured prediction using Gaussian processes
We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M ^3 N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct
Bayesian Structured Prediction Using Gaussian Processes
We introduce a conceptually novel structured prediction model, GPstruct,
which is kernelized, non-parametric and Bayesian, by design. We motivate the
model with respect to existing approaches, among others, conditional random
fields (CRFs), maximum margin Markov networks (M3N), and structured support
vector machines (SVMstruct), which embody only a subset of its properties. We
present an inference procedure based on Markov Chain Monte Carlo. The framework
can be instantiated for a wide range of structured objects such as linear
chains, trees, grids, and other general graphs. As a proof of concept, the
model is benchmarked on several natural language processing tasks and a video
gesture segmentation task involving a linear chain structure. We show
prediction accuracies for GPstruct which are comparable to or exceeding those
of CRFs and SVMstruct.This is the accepted manuscript version. The final version is available from IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6942234
Use of freely available datasets and machine learning methods in predicting deforestation
The range and quality of freely available geo-referenced datasets is increasing. We evaluate the usefulness of free datasets for deforestation prediction by comparing generalised linear models and generalised linear mixed models (GLMMs) with a variety of machine learning models (Bayesian networks, artificial neural networks and Gaussian processes) across two study regions. Freely available datasets were able to generate plausible risk maps of deforestation using all techniques for study zones in both Mexico and Madagascar. Artificial neural networks outperformed GLMMs in the Madagascan (average AUC 0.83 vs 0.80), but not the Mexican study zone (average AUC 0.81 vs 0.89). In Mexico and Madagascar, Gaussian processes (average AUC 0.89, 0.85) and structured Bayesian networks (average AUC 0.88, 0.82) performed at least as well as GLMMs (average AUC 0.89, 0.80). Bayesian networks produced more stable results across different sampling methods. Gaussian processes performed well (average AUC 0.85) with fewer predictor variables
Forecasting of commercial sales with large scale Gaussian Processes
This paper argues that there has not been enough discussion in the field of
applications of Gaussian Process for the fast moving consumer goods industry.
Yet, this technique can be important as it e.g., can provide automatic feature
relevance determination and the posterior mean can unlock insights on the data.
Significant challenges are the large size and high dimensionality of commercial
data at a point of sale. The study reviews approaches in the Gaussian Processes
modeling for large data sets, evaluates their performance on commercial sales
and shows value of this type of models as a decision-making tool for
management.Comment: 1o pages, 5 figure
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Probabilistic matrix factorization (PMF) is a powerful method for modeling
data associated with pairwise relationships, finding use in collaborative
filtering, computational biology, and document analysis, among other areas. In
many domains, there is additional information that can assist in prediction.
For example, when modeling movie ratings, we might know when the rating
occurred, where the user lives, or what actors appear in the movie. It is
difficult, however, to incorporate this side information into the PMF model. We
propose a framework for incorporating side information by coupling together
multiple PMF problems via Gaussian process priors. We replace scalar latent
features with functions that vary over the space of side information. The GP
priors on these functions require them to vary smoothly and share information.
We successfully use this new method to predict the scores of professional
basketball games, where side information about the venue and date of the game
are relevant for the outcome.Comment: 18 pages, 4 figures, Submitted to UAI 201
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