Yasutoshi Yajima

577 total citations
22 papers, 395 citations indexed

About

Yasutoshi Yajima is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Yasutoshi Yajima has authored 22 papers receiving a total of 395 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Numerical Analysis, 9 papers in Computational Theory and Mathematics and 8 papers in Artificial Intelligence. Recurrent topics in Yasutoshi Yajima's work include Advanced Optimization Algorithms Research (11 papers), Optimization and Variational Analysis (7 papers) and Face and Expression Recognition (7 papers). Yasutoshi Yajima is often cited by papers focused on Advanced Optimization Algorithms Research (11 papers), Optimization and Variational Analysis (7 papers) and Face and Expression Recognition (7 papers). Yasutoshi Yajima collaborates with scholars based in Japan. Yasutoshi Yajima's co-authors include Hiroshi Konno, Takahito Kuno, Tomomi Matsui, Tetsuya Fujie, Yoshitsugu Yamamoto, Masao Mori, Mikio Kubo, Motakuri V. Ramana, Takao Enkawa and Pãnos M. Pardalos and has published in prestigious journals such as European Journal of Operational Research, International Journal of Intelligent Systems and Journal of Global Optimization.

In The Last Decade

Yasutoshi Yajima

19 papers receiving 372 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yasutoshi Yajima Japan 9 286 261 137 53 47 22 395
Laura Martein Italy 9 243 0.8× 276 1.1× 168 1.2× 27 0.5× 33 0.7× 32 480
Hong-Xuan Huang China 11 219 0.8× 288 1.1× 193 1.4× 54 1.0× 52 1.1× 18 445
Phan Thiên Thach Vietnam 10 285 1.0× 290 1.1× 92 0.7× 22 0.4× 58 1.2× 27 407
Alberto Cambini Italy 9 197 0.7× 238 0.9× 132 1.0× 19 0.4× 26 0.6× 20 427
István Maros United Kingdom 10 167 0.6× 157 0.6× 61 0.4× 55 1.0× 39 0.8× 25 347
Hongwei Jiao China 16 521 1.8× 468 1.8× 275 2.0× 79 1.5× 31 0.7× 54 622
Josef Nedoma Czechia 6 129 0.5× 238 0.9× 233 1.7× 41 0.8× 22 0.5× 12 497
Jiayu Zhang China 12 192 0.7× 257 1.0× 48 0.4× 16 0.3× 41 0.9× 22 362
Wenxun Xing China 12 173 0.6× 176 0.7× 87 0.6× 50 0.9× 208 4.4× 50 466
T.-H. Shiau United States 6 156 0.5× 197 0.8× 55 0.4× 23 0.4× 19 0.4× 10 350

Countries citing papers authored by Yasutoshi Yajima

Since Specialization
Citations

This map shows the geographic impact of Yasutoshi Yajima's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yasutoshi Yajima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yasutoshi Yajima more than expected).

Fields of papers citing papers by Yasutoshi Yajima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yasutoshi Yajima. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yasutoshi Yajima. The network helps show where Yasutoshi Yajima may publish in the future.

Co-authorship network of co-authors of Yasutoshi Yajima

This figure shows the co-authorship network connecting the top 25 collaborators of Yasutoshi Yajima. A scholar is included among the top collaborators of Yasutoshi Yajima based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yasutoshi Yajima. Yasutoshi Yajima is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yajima, Yasutoshi, et al.. (2007). Semi-supervised Kernel Logistic Regression and Its Extension to Active Learning Based on A-Optimality. 6. 277–282. 13 indexed citations
2.
Yajima, Yasutoshi. (2007). Predicting purchase preferences using semi-supervised one-class SVM with graph kernels. 14. 3505–3511. 1 indexed citations
3.
Yajima, Yasutoshi, et al.. (2006). Optimization Approaches for Semi-Supervised Multiclass Classification. 16. 863–867. 1 indexed citations
4.
Yajima, Yasutoshi, et al.. (2006). Efficient Formulations for 1-SVM and their Application to Recommendation Tasks. Journal of Computers. 1(3). 7 indexed citations
5.
Yajima, Yasutoshi, et al.. (2006). Optimization Approaches for Semi-Supervised Learning. 14. 247–252. 2 indexed citations
6.
Yajima, Yasutoshi, et al.. (2005). Ranking and selecting terms for text categorization via SVM discriminate boundary. International Journal of Intelligent Systems. 25(2). 496–501 Vol. 2. 11 indexed citations
7.
Yajima, Yasutoshi. (2003). Linear programming approaches for multicategory support vector machines. European Journal of Operational Research. 162(2). 514–531. 20 indexed citations
8.
Yajima, Yasutoshi, et al.. (2003). EXTRACTING FEATURE SUBSPACE FOR KERNEL BASED LINEAR PROGRAMMING SUPPORT VECTOR MACHINES. Journal of the Operations Research Society of Japan. 46(4). 395–408. 2 indexed citations
9.
Yajima, Yasutoshi. (2002). Positive semidefinite relaxations for distance geometry problems. Japan Journal of Industrial and Applied Mathematics. 19(1). 87–112. 3 indexed citations
10.
Yajima, Yasutoshi, Motakuri V. Ramana, & Pãnos M. Pardalos. (2001). Cuts and SemidefiniteRelaxations for N onconvex Quadratic Problems. 1 indexed citations
11.
Yajima, Yasutoshi & Hiroshi Konno. (1999). An algorithm for a concave production cost network flow problem. Japan Journal of Industrial and Applied Mathematics. 16(2). 243–256. 2 indexed citations
12.
Yajima, Yasutoshi & Tetsuya Fujie. (1998). A Polyhedral Approach for Nonconvex Quadratic Programming Problems with Box Constraints. Journal of Global Optimization. 13(2). 151–170. 31 indexed citations
13.
Kubo, Mikio, et al.. (1996). A SPLIT DELIVERY VEHICLE ROUTING PROBLEM. Journal of the Operations Research Society of Japan. 39(3). 372–388. 5 indexed citations
14.
Yajima, Yasutoshi & Hiroshi Konno. (1995). OUTER APPROXIMATION ALGORITHMS FOR LOWER RANK BILINEAR PROGRAMMING PROBLEMS. Journal of the Operations Research Society of Japan. 38(2). 230–239. 5 indexed citations
15.
Konno, Hiroshi, Takahito Kuno, & Yasutoshi Yajima. (1994). Global minimization of a generalized convex multiplicative function. Journal of Global Optimization. 4(1). 47–62. 65 indexed citations
16.
Kuno, Takahito, Yasutoshi Yajima, Yoshitsugu Yamamoto, & Hiroshi Konno. (1994). Convex programs with an additional constraint on the product of several convex functions. European Journal of Operational Research. 77(2). 314–324. 8 indexed citations
17.
Konno, Hiroshi, et al.. (1994). Calculating a minimal sphere containing a polytope defined by a system of linear inequalities. Computational Optimization and Applications. 3(2). 181–191. 3 indexed citations
18.
Kuno, Takahito, Yasutoshi Yajima, & Hiroshi Konno. (1993). An outer approximation method for minimizing the product of several convex functions on a convex set. Journal of Global Optimization. 3(3). 325–335. 52 indexed citations
19.
Konno, Hiroshi, Takahito Kuno, & Yasutoshi Yajima. (1992). Parametric simplex algorithms for a class of NP-Complete problems whose average number of steps is polynomial. Computational Optimization and Applications. 1(2). 227–239. 23 indexed citations
20.
Konno, Hiroshi, Yasutoshi Yajima, & Tomomi Matsui. (1991). Parametric simplex algorithms for solving a special class of nonconvex minimization problems. Journal of Global Optimization. 1(1). 65–81. 126 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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