[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[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