FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2964229844> ?p ?o ?g. }

• W2964229844 endingPage "5486" @default.
• W2964229844 startingPage "5471" @default.
• W2964229844 abstract "In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We use a tree notion in order to partition the space of regressors in a nested structure. The introduced algorithms adapt not only their regression functions but also the complete tree structure while achieving the performance of the “best” linear mixture of a doubly exponential number of partitions, with a computational complexity only polynomial in the number of nodes of the tree. While constructing these algorithms, we also avoid using any artificial “weighting” of models (with highly data dependent parameters) and, instead, directly minimize the final regression error, which is the ultimate performance goal. The introduced methods are generic such that they can readily incorporate different tree construction methods such as random trees in their framework and can use different regressor or partitioning functions as demonstrated in the paper." @default.
• W2964229844 created "2019-07-30" @default.
• W2964229844 creator A5061392794 @default.
• W2964229844 creator A5089040739 @default.
• W2964229844 date "2014-10-01" @default.
• W2964229844 modified "2023-10-17" @default.
• W2964229844 title "A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees" @default.
• W2964229844 cites W1965130726 @default.
• W2964229844 cites W1966048296 @default.
• W2964229844 cites W1978071126 @default.
• W2964229844 cites W1980145033 @default.
• W2964229844 cites W1982840058 @default.
• W2964229844 cites W1988526400 @default.
• W2964229844 cites W2007233728 @default.
• W2964229844 cites W2013593838 @default.
• W2964229844 cites W2014569347 @default.
• W2964229844 cites W2017496074 @default.
• W2964229844 cites W2061233009 @default.
• W2964229844 cites W2065927882 @default.
• W2964229844 cites W2067021215 @default.
• W2964229844 cites W2074521042 @default.
• W2964229844 cites W2080240714 @default.
• W2964229844 cites W2089089572 @default.
• W2964229844 cites W2096352571 @default.
• W2964229844 cites W2099830893 @default.
• W2964229844 cites W2103644547 @default.
• W2964229844 cites W2108258277 @default.
• W2964229844 cites W2109035688 @default.
• W2964229844 cites W2109270363 @default.
• W2964229844 cites W2117250207 @default.
• W2964229844 cites W2119328597 @default.
• W2964229844 cites W2119596571 @default.
• W2964229844 cites W2120135036 @default.
• W2964229844 cites W2122963687 @default.
• W2964229844 cites W2129160848 @default.
• W2964229844 cites W2133472798 @default.
• W2964229844 cites W2141394518 @default.
• W2964229844 cites W2148746383 @default.
• W2964229844 cites W2153946895 @default.
• W2964229844 cites W2155817051 @default.
• W2964229844 cites W2161597212 @default.
• W2964229844 cites W2163294786 @default.
• W2964229844 cites W2163579114 @default.
• W2964229844 cites W2174015049 @default.
• W2964229844 cites W2498119267 @default.
• W2964229844 cites W2532600617 @default.
• W2964229844 cites W3101533025 @default.
• W2964229844 cites W4247131500 @default.
• W2964229844 cites W4249599117 @default.
• W2964229844 doi "https://doi.org/10.1109/tsp.2014.2349882" @default.
• W2964229844 hasPublicationYear "2014" @default.
• W2964229844 type Work @default.
• W2964229844 sameAs 2964229844 @default.
• W2964229844 citedByCount "56" @default.
• W2964229844 countsByYear W29642298442014 @default.
• W2964229844 countsByYear W29642298442015 @default.
• W2964229844 countsByYear W29642298442016 @default.
• W2964229844 countsByYear W29642298442017 @default.
• W2964229844 countsByYear W29642298442018 @default.
• W2964229844 countsByYear W29642298442019 @default.
• W2964229844 countsByYear W29642298442020 @default.
• W2964229844 countsByYear W29642298442021 @default.
• W2964229844 countsByYear W29642298442022 @default.
• W2964229844 countsByYear W29642298442023 @default.
• W2964229844 crossrefType "journal-article" @default.
• W2964229844 hasAuthorship W2964229844A5061392794 @default.
• W2964229844 hasAuthorship W2964229844A5089040739 @default.
• W2964229844 hasBestOaLocation W29642298442 @default.
• W2964229844 hasConcept C105795698 @default.
• W2964229844 hasConcept C113174947 @default.
• W2964229844 hasConcept C11413529 @default.
• W2964229844 hasConcept C114614502 @default.
• W2964229844 hasConcept C120068334 @default.
• W2964229844 hasConcept C126255220 @default.
• W2964229844 hasConcept C126838900 @default.
• W2964229844 hasConcept C134306372 @default.
• W2964229844 hasConcept C163797641 @default.
• W2964229844 hasConcept C164660894 @default.
• W2964229844 hasConcept C17095337 @default.
• W2964229844 hasConcept C183115368 @default.
• W2964229844 hasConcept C197855036 @default.
• W2964229844 hasConcept C2524010 @default.
• W2964229844 hasConcept C33923547 @default.
• W2964229844 hasConcept C35519122 @default.
• W2964229844 hasConcept C41008148 @default.
• W2964229844 hasConcept C42812 @default.
• W2964229844 hasConcept C71924100 @default.
• W2964229844 hasConcept C83546350 @default.
• W2964229844 hasConceptScore W2964229844C105795698 @default.
• W2964229844 hasConceptScore W2964229844C113174947 @default.
• W2964229844 hasConceptScore W2964229844C11413529 @default.
• W2964229844 hasConceptScore W2964229844C114614502 @default.
• W2964229844 hasConceptScore W2964229844C120068334 @default.
• W2964229844 hasConceptScore W2964229844C126255220 @default.
• W2964229844 hasConceptScore W2964229844C126838900 @default.
• W2964229844 hasConceptScore W2964229844C134306372 @default.
• W2964229844 hasConceptScore W2964229844C163797641 @default.
• W2964229844 hasConceptScore W2964229844C164660894 @default.

• next