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Jian Fang

6 papers hereh-index 7690 citations16 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author2

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.IT2
same name
  • Jian Fang — 34 papers, h 18
  • Jian Fang — 7 papers, h 18
  • Jian Fang — 6 papers, h 4
  • Jian Fang — 3 papers, h 12
  • Jian Fang — 3 papers, h 4
  • Jian Fang — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132018
most citedFast Compressed Sensing SAR Imaging based on Approximated Observation

266 citations · 273 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2018

Learning through deterministic assignment of hidden parameters

Jian Fang, Shaobo Lin, Zongben Xu

Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameter…

cs.LG2014

Learning and approximation capability of orthogonal super greedy algorithm

Jian Fang, Shaobo Lin, Zongben Xu

We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning. OSGA is concerned with selecting more than one at…

cs.LG2014★ 4 cited

Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)

Shaobo Lin, Xia Liu, Jian Fang +1

An extreme learning machine (ELM) can be regarded as a two stage feed-forward neural network (FNN) learning system which randomly assigns the connections with and within hidden neu…

cs.LG2013

Learning rates of lq coefficient regularization learning with Gaussian kernel

Shaobo Lin, Jinshan Zeng, Jian Fang +1

Regularization is a well recognized powerful strategy to improve the performance of a learning machine and lq regularization schemes with 0<q<∞ are central in use. It is…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.