Ryohei Nakano's Refereed Journal Papers
[J01] R. Nakano and K. Saito:
Reduction of aggregate functions in relational calculus alpha
to optimal algebraic expressions (in Japanese),
Trans. of IPSJ, Vol.28, No.12, pp.1246-1254 (1987).
[J02] R. Nakano and K. Saito:
Reduction of relational calculus to optimal algebraic expression
(in Japanse),
Trans. of IPSJ, Vol.29, No.4, pp.397-406 (1988).
[J03] R. Nakano:
Translation with optimization from relational calculus to
relational algebra having aggregate functions,
ACM Trans. on Database Syst., Vol.15, No.4, pp.518-557 (1990).
[J04] K. Saito and R. Nakano:
Rule extraction from facts: RF2 algorithm (in Japanse),
Trans. of IPSJ, Vol.33, No.5, pp.628-635 (1992).
[J05] K. Saito and R. Nakano:
Rule extraction from noisy facts: RF3 algorithm (in Japanese),
Trans. of IPSJ, Vol.33, No.5, pp.636-644 (1992).
[J06] T. Yamada, B.E. Rosen, and R. Nakano:
Critical block simulated annealing for job shop scheduling (in Japanese),
Trans. of IEE of Japan, Vol.114ーC, No.4, pp.476-482 (1994).
[J07] N. Ueda and R. Nakano:
Competitive and selective learning method for vector quantizer design
- equidistortion principle and its algorithm
(in Japanese),
IEICE Trans., Vol.J77-D-2, No.11, pp.2265-2278 (1994).
[J08] N. Ueda and R. Nakano:
A new competitive learning approach based on an equidistortion
principle for designing optimal vector quantizers,
Neural Networks, Vol.7, No.8, pp.1211-1227 (1994).
[J09] K. Saito and R. Nakano:
Task sequencing based on Bayesian estimation (in Japanese),
Trans. of IPSJ, Vo.36, No.3, pp.572-578 (1995).
[J10] K. Saito and R. Nakano:
Adaptive concept learning algorithm: RF4 (in Japanese),
Trans. of IPSJ, Vo.36, No.4, pp.832-839 (1995).
[J11] T. Yamada and R. Nakano:
Job-shop scheduling by simulated annealing combined with
deterministic local search (in Japanese),
Trans. of IPSJ, Vol.37, No.4, pp.597-604 (1996).
[J12] K. Saito and R. Nakano:
A connectionist approach to numeric law discovery (in Japanese),
Trans. of IPSJ, Vol.37, No.9, pp.1708-1716 (1996).
[J13] K. Saito and R. Nakano:
Partial BFGS update and efficient
step-length calculation for three-layer neural networks,
Neural Computation, Vol.9, No.1, pp.239-257 (1997).
[J14] N. Ueda and R. Nakano:
Deterministic annealing EM algorithm (in Japanese),
IEICE Trans., Vol.J80-D-2, No.1, pp.267-276 (1997).
[J15] T. Yamada and R. Nakano:
Job-shop scheduling by genetic local search (in Japanese),
IEICE Trans., Vo.38, No.6, pp.1126-1138 (1997).
[J16] N. Ueda and R. Nakano:
Analysis of generalization error on
ensemble learning (in Japanese),
IEICE Trans., Vol.J80-D-2, No.9, pp.2512-2521 (1997).
[J17] K. Saito and R. Nakano:
Second-order learning algorithm with squared penalty term
(in Japanese),
Trans. of IPSJ, Vol.38, No.11, pp.2149-2156 (1997).
[J18] K. Saito and R. Nakano:
A new regularization based on the MDL principle (in Japanese),
Trans. of JSAI, Vol.13, No.1, pp.123-130 (1998).
[J19] K. Saito and R. Nakano:
A constructive learning algorithm for HME (in Japanese),
IEICE Trans., Vol.J81-D-2, No.2, pp.404-411 (1998).
[J20] K. Saito and R. Nakano:
Applying second-order learning algorithm BPQ to
classification problems and evaluating it (in Japanese),
IEICE Trans., Vol.J81-D-2, No.2, pp.412-420 (1998).
[J21] K. Saito and R. Nakano:
Learning of recurrent networks and estimation of Gaussian mixtures
using second-order learning algorithm
BPQ (in Japanese),
IEICE Trans., Vol.J81-D-2, No.3, pp.538-546 (1998).
[J22] N. Ueda and R. Nakano:
Deterministic annealing EM algorithm,
Neural Networks, Vol.11, No.2, pp.271-282 (1998).
[J23] M. Kimura and R. Nakano:
Learning dynamical systems produced by continuous time
recurrent neural networks,
Neural Networks, Vol.11, No.2, pp.271-282 (1998).
[J24] M. Kimura and R. Nakano:
Dynamical systems produced by recurrent neural netowrks
(in Japanese),
IEICE Trans., Vol.J82-D-2, No.4, pp.818-828 (1999).
[J25] N. Ueda and R. Nakano:
EM algorithm with split and merge operations for mixture models
(in Japanese),
IEICE Trans., Vol.J82-D-2, No.5, pp.930-940 (1999).
[J26] K. Arai and R. Nakano:
Adaptive beta scheduling learning of finite state machine
by recurrent neural networks (in Japanese),
IEICE Trans., Vol.J82-D-2, No.6, pp.1082-1092 (1999).
[J27] M. Kimura and R.Nakano:
A unique representation of affine neural dynamical systems
(in Japanese),
JSIAM Trans., Vol.9, No.2, pp.37-50 (1999).
[J28] K. Arai and R. Nakano:
Baysian learning of finite state machine with consideration
of internal state representation (in Japanese),
IEICE Trans., J82-D-2, No.11, pp.2101-2110 (1999).
[J29] N. Ueda and R. Nakano:
Pattern recognition by mixture of factor analyzers -
probabilistic mixture subspace method (in Japanese),
IEICE Trans., Vol.J82-D-2, No.12, pp.2394-2401 (1999).
[J30] K. Saito and R. Nakano:
Second-order learning algorithm with squared penalty term,
Neural Computation, Vol.12, No.3, pp.709-729 (2000).
[J31] N. Ueda, R. Nakano, Z. Ghahramani and G.E. Hinton:
SMEM algorithm for mixture models,
Neural Computation, Vol.12, No.9, pp.2109-2128 (2000).
[J32] K. Arai and R. Nakano:
Stable behavior in a recurrent neural network for a finite state machine,
Neural Networks, Vol.13, No.6, pp.667-680 (2000).
[J33] N. Ueda and R. Nakano and Z. Ghahramani and G.E. Hinton:
Split and merge EM algorithm for improving Gaussian mixture density estimates,
Journal of VLSI Signal Processing, Vol.26, pp.133-140 (2000).
[J34] N. Ueda and R. Nakano:
EM algorithm with split and merge operations for mixture models,
IEICE Trans. Inf & Syst, Vol.E83-D, No.12, pp.2047-2055 (2000).
[J35] K.Saito and R.Nakano:
Discovery of relevant weights by minimizing cross-validation error
(in Japanese),
IEICE Trans., Vol.J84-D-2, No.1, pp.178-187 (2001).
[J36] R. Nakano and K. Saito:
Discovering polynomials to fit multivariate data having numeric and
nominal variables,
LNAI 2281, pp.482-493 (2002).
[J37] K. Saito and R. Nakano:
Extracting regression rules from neural networks,
Neural Networks, Vol.15, No.10, pp.1279-1288 (2003).
[J38] K. Saito and R. Nakano:
Squared penalty consistent with linear transformations of variables
(in Japanese),
Trans. of IPSJ, Vol.44, No.10, pp.2495-2502 (2003).
[J39] K. Saito and R. Nakano:
Bidirectional clustering of weights for neural networks with common weights
(in Japanese),
IEICE Trans., Vol.J88-D-2, No.4, pp.789-799 (2005).
[J40] M. Kimura, K. Saito and R. Nakano:
Efficient finding of influential nodes from a social network
(in Japanese),
IEICE Trans., Vol.J91-D, No.4, pp.1004-1015 (2008).
[J41] Y. Tanahashi and R. Nakano:
Bidirectional clustering of weights
for finding succinct multivariate polynomials,
International Journal of Computer Science and Network Security,
Vol.8, No.5, pp.85-94 (2008).
[J42] Y. Tanahashi, R. Nakano and K. Saito:
Nominally conditioned multiple regression by using a four-layer perceptron
(in Japanese),
IEICE Trans., Vol.J91-D, No.8, pp.2166-2176 (2008).
[J43] M. Karasuyama, I. Takauchi and R. Nakano:
Efficient leave-m-out cross-validation of support vector regression
by generalizing decremental algorithm,
New Generation Computing, Vol.27, No.4, Special Issue on
Data-Mining and Statistical Science, pp.307-318 (2009).
[J44] M. Kimura, K. Saito, R. Nakano, H. Motoda:
Learning information diffusion model for extracting influential nodes
in a social network (in Japanese),
Trans. of JSAI, Vol.25, No.1, pp.215-223 (2010).
[J45] M. Kimura, K. Saito, R. Nakano, and H. Motoda,
Extracting influential nodes in a social network for information diffusion,
Data Mining and Knowledge Discovery, Vol.20, pp.70-97 (2010).
[J46] Y. Ishikawa, I. Takeuchi, and R. Nakano,
Multi-directional search from the primitive initial point for
Gaussian mixture estimation using variational Bayes method,
Neural Networks, Vol.23, No.3, pp.356-364 (2010).
[J47] S. Suzumura and R. Nakano:
Complex-valued BFGS method for complex-valued neural networks (in Japanese),
IEICE Trans., Vol.J96-D, No.3, pp.423-431 (2013).
(rated as excellent paper in student papers issue)
[J48] R. Nakano:
Error correction of enumerative induction of deterministic context-free L-system grammar,
IAENG International Journal of Computer Science, Vol.40, No.1, pp.47--52 (2013).
[J49] S. Satoh and R. Nakano:
Fast and stable learning utilizing singular regions of multilayer perceptron,
Neural Processing Letters, vol.38, No.2, pp.99-115,
Doi: 10.1007/s11063-013-9283-z, Springer (2013).
final version is here.
[J50] S. Satoh and R. Nakano:
Search pruning for a search method utilizing singular regions of multilayer perceptrons (in Japanese),
IEICE Trans., Vol.J97-D, No.2, pp.330-340 (2014).
[J51] S. Satoh and R. Nakano:
How learning methods influence the performance of complex-valued multilayer perceptrons (in Japanese),
IEICE Trans., Vol.J100-D, No.6, pp.649-660, Doi: 10.14923/transinfj.2016JDP7109 (2017).
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Last modified on: July 22, 2019