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L. Tran

6 papers hereh-index 9193 citations26 works total

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

author position
  • sole author1
  • first author4

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • stat.ML4
  • cs.CV1
  • q-fin.ST1
same name
  • L. Tran — 9 papers, h 9
  • L. Tran — 8 papers, h 9
  • L. Tran — 2 papers, h 3
  • L. Tran — 1 paper, h 25
  • L. Tran — 1 paper, h 8
  • L. Tran — 1 paper, 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
20122019
most citedHypergraph and protein function prediction with gene expression data

6 citations · 6 across the 1 of their papers we have counts for

collaborators
Showing stat.MLShow all

4 papers · 1 filter

stat.ML2019

Solve fraud detection problem by using graph based learning methods

Loc Tran, Tuan Tran, Linh Tran +1

The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss…

stat.ML2018

Un-normalized hypergraph p-Laplacian based semi-supervised learning methods

Loc Hoang Tran, Linh Hoang Tran

Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship bet…

stat.ML2018

Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approach

Loc Hoang Tran, Trang Hoang, Bui Hoang Nam Huynh

Most network-based speech recognition methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. However, assuming…

stat.ML2012★ 6 cited

Hypergraph and protein function prediction with gene expression data

Loc Tran

Most network-based protein (or gene) function prediction methods are based on the assumption that the labels of two adjacent proteins in the network are likely to be the same. Howe…

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