21 papers · ranked by Valyu relevance
Susanne Trick, Constantin A. Rothkopf
Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling. Here we present a hierarchical Bayesian model of probabilistic classifier fusion based on a new…
Eugene Santos Jr., Jacob Jurmain, Anthony Ragazzi, Alexander Zimper
The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real…
Ryan S. Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts
Combining several (sample approximations of) distributions, which we term sub-posteriors, into a single distribution proportional to their product, is a common challenge. Occurring, for instance, in distributed 'big data' problems, or when working under multi-party privacy constraints. Many existing approaches resort…
Gautam Thakur
This paper develops a mathematical and computational framework for analyzing the expected performance of Bayesian data fusion, or joint statistical inference, within a sensor network. We use variational techniques to obtain the posterior expectation as the optimal fusion rule under a deterministic constraint and a…
Da Zhi, Ladan Shahshahani, Caroline Nettekoven, Ana Lúısa Pinho + 2 more
One important barrier in the development of complex models of human brain organization is the lack of a large and comprehensive task-based neuro-imaging dataset. Therefore, current atlases of functional brain organization are mainly based on single and homogeneous resting-state datasets. Here, we propose a hierarchical…
Pablo Torrijos, José M. Puerta, José A. Gámez, Juan A. Aledo
This paper presents the Greedy Min-Cut Bayesian Consensus (GMCBC) algorithm for the structural fusion of Bayesian Networks (BNs). The method is designed to preserve essential dependencies while controlling network complexity. It addresses the limitations of traditional fusion approaches, which often lead to excessively…
Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga
Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are leading to the proposal of new methods and paradigms, in order to be able to deal with what is nowadays referred to as Big Data. In this paper…
Máté Aller, Uta Noppeney
To form a percept of the environment, the brain needs to solve the binding problem – inferring whether signals come from a common cause and be integrated, or come from independent causes and be segregated. Behaviourally, humans solve this problem near-optimally as predicted by Bayesian Causal Inference; but, the neural…
Silvia Beddar-Wiesing, Maarten Bieshaar
Fusion is a common tool for the analysis and utilization of available datasets and so an essential part of data mining and machine learning processes. However, a clear definition of the type of fusion is not always provided due to inconsistent literature. In the following, the process of fusion is defined depending on…
Marcus Ghosh, Gabriel Béna, Volker Bormuth, Dan F. M. Goodman
Animals continuously detect information via multiple sensory channels, like vision and hearing, and integrate these signals to realise faster and more accurate decisions; a fundamental neural computation known as multisensory integration. A widespread view of this process is that multimodal neurons linearly fuse…
Günther Koliander, Yousef El-Laham, Petar M. Djurić, Franz Hlawatsch
—Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous random variable or vector. Although the case of continuous random…
Yilin Dong, Xinde Li, Yihai Liu, Yong Deng
In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on…
Tim Rohe, Ann-Christine Ehlis, Uta Noppeney
Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem – deciding whether signals come from a common cause and should be integrated, or instead be segregated. Human observers typically arbitrate between integration and segregation consistent with Bayesian…
Justina Žurauskienė, Paul DW Kirk, Michael PH Stumpf
The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. A variety of computational techniques allow us to analyse such data and to model the biological processes behind them. However, it is increasingly being…
Wenyu Zhang, Zhenjiang Zhang, Leonhard M. Reindl
Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective…
Sandra Rothe, Bastian Kudszus, Dirk Söffker
The reliability of complex or safety critical systems is of increasing importance in several application fields. In many cases, decisions evaluating situations or conditions are made. To ensure the high accuracy of these decisions, the assignments from different classifiers can be fused to one final decision to improve…
Yujie Chen, Zexi Hua, Yongchuan Tang, Baoxin Li + 1 more
Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Identifying molecular structure based on spectroscopic readings is a key task in a va- riety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless…