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Gaussian process\nclassification provides a principled approach, but the corresponding\ncomputational burden is hardly sustainable in large-scale problems and devising\nefficient alternatives is a challenge. In this work, we investigate if and how\nGaussian process regression directly applied to the classification labels can\nbe used to tackle this question. While in this case training time is remarkably\nfaster, predictions need be calibrated for classification and uncertainty\nestimation. To this aim, we propose a novel approach based on interpreting the\nlabels as the output of a Dirichlet distribution. Extensive experimental\nresults show that the proposed approach provides essentially the same accuracy\nand uncertainty quantification of Gaussian process classification while\nrequiring only a fraction of computational resources.","url_abs":"http://arxiv.org/abs/1805.10915v1","url_pdf":"http://arxiv.org/pdf/1805.10915v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dirichlet-based-gaussian-processes-for-large","repo_url":"https://github.com/dmilios/dirichletGPC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.10915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.10915"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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