Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313447450> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W4313447450 abstract "The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen and designed based on criteria which are often not clearly stated, for problem settings not clearly defined, tested in unrealistic settings, and/or in isolation from related approaches in the wider literature. This puts into question the potential for real-world impact of many approaches conceived in such contexts, and risks propagating a misguided research focus. We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks. Through and in reflection of this formulation and overview, helped by an informal survey of industrial players dealing with real-world data streams, we provide recommendations. Our main emphasis is that learning from data streams does not impose a single-pass or online-learning approach, or any particular learning regime; and any constraints on memory and time are not specific to streaming. Meanwhile, there exist established techniques for dealing with temporal dependence and concept drift, in other areas of the literature. For the data streams community, we thus encourage a shift in research focus, from dealing with often-artificial constraints and assumptions on the learning mode, to issues such as robustness, privacy, and interpretability which are increasingly relevant to learning in data streams in academic and industrial settings." @default.
- W4313447450 created "2023-01-06" @default.
- W4313447450 creator A5037208593 @default.
- W4313447450 creator A5055668668 @default.
- W4313447450 date "2022-12-30" @default.
- W4313447450 modified "2023-10-11" @default.
- W4313447450 title "Learning from Data Streams: An Overview and Update" @default.
- W4313447450 doi "https://doi.org/10.48550/arxiv.2212.14720" @default.
- W4313447450 hasPublicationYear "2022" @default.
- W4313447450 type Work @default.
- W4313447450 citedByCount "0" @default.
- W4313447450 crossrefType "posted-content" @default.
- W4313447450 hasAuthorship W4313447450A5037208593 @default.
- W4313447450 hasAuthorship W4313447450A5055668668 @default.
- W4313447450 hasBestOaLocation W43134474501 @default.
- W4313447450 hasConcept C119857082 @default.
- W4313447450 hasConcept C120665830 @default.
- W4313447450 hasConcept C121332964 @default.
- W4313447450 hasConcept C124101348 @default.
- W4313447450 hasConcept C151730666 @default.
- W4313447450 hasConcept C154945302 @default.
- W4313447450 hasConcept C162324750 @default.
- W4313447450 hasConcept C187736073 @default.
- W4313447450 hasConcept C192209626 @default.
- W4313447450 hasConcept C2522767166 @default.
- W4313447450 hasConcept C2777611316 @default.
- W4313447450 hasConcept C2778484313 @default.
- W4313447450 hasConcept C2779343474 @default.
- W4313447450 hasConcept C2780451532 @default.
- W4313447450 hasConcept C41008148 @default.
- W4313447450 hasConcept C60777511 @default.
- W4313447450 hasConcept C76155785 @default.
- W4313447450 hasConcept C86803240 @default.
- W4313447450 hasConcept C89198739 @default.
- W4313447450 hasConceptScore W4313447450C119857082 @default.
- W4313447450 hasConceptScore W4313447450C120665830 @default.
- W4313447450 hasConceptScore W4313447450C121332964 @default.
- W4313447450 hasConceptScore W4313447450C124101348 @default.
- W4313447450 hasConceptScore W4313447450C151730666 @default.
- W4313447450 hasConceptScore W4313447450C154945302 @default.
- W4313447450 hasConceptScore W4313447450C162324750 @default.
- W4313447450 hasConceptScore W4313447450C187736073 @default.
- W4313447450 hasConceptScore W4313447450C192209626 @default.
- W4313447450 hasConceptScore W4313447450C2522767166 @default.
- W4313447450 hasConceptScore W4313447450C2777611316 @default.
- W4313447450 hasConceptScore W4313447450C2778484313 @default.
- W4313447450 hasConceptScore W4313447450C2779343474 @default.
- W4313447450 hasConceptScore W4313447450C2780451532 @default.
- W4313447450 hasConceptScore W4313447450C41008148 @default.
- W4313447450 hasConceptScore W4313447450C60777511 @default.
- W4313447450 hasConceptScore W4313447450C76155785 @default.
- W4313447450 hasConceptScore W4313447450C86803240 @default.
- W4313447450 hasConceptScore W4313447450C89198739 @default.
- W4313447450 hasLocation W43134474501 @default.
- W4313447450 hasLocation W43134474502 @default.
- W4313447450 hasOpenAccess W4313447450 @default.
- W4313447450 hasPrimaryLocation W43134474501 @default.
- W4313447450 hasRelatedWork W2060628068 @default.
- W4313447450 hasRelatedWork W2601363847 @default.
- W4313447450 hasRelatedWork W2759864402 @default.
- W4313447450 hasRelatedWork W2964491809 @default.
- W4313447450 hasRelatedWork W2980415170 @default.
- W4313447450 hasRelatedWork W2981673118 @default.
- W4313447450 hasRelatedWork W3208495060 @default.
- W4313447450 hasRelatedWork W4281572076 @default.
- W4313447450 hasRelatedWork W4307392573 @default.
- W4313447450 hasRelatedWork W4375817846 @default.
- W4313447450 isParatext "false" @default.
- W4313447450 isRetracted "false" @default.
- W4313447450 workType "article" @default.