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What is the composition of dear? 

Modernism
Gaussian process
Model selection
Circle of Friends
Kaonic hydrogen
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The composition of DEAR (Differential Equation Associated Regression) models consists of two main components: a random function with a linear difference equation-wise regression as the central tendency and a variance bound specified by Gaussian error analysis theory . DEAR models were originally proposed on the random fuzzy theoretical foundation but can be defined on any measure theoretic platform . In the context of statistical machine learning (SML) algorithm developments, DEAR models can contribute significantly, particularly in developing robot movement systems where motion laws are expressed by differential equations . The DEAR learning algorithm incorporates a λ-global optimization scheme, making it one of the fastest, most efficient, and accurate SML algorithms .

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Papers (4)Insight
Journal Article•DOI
The DEAR case
Ross King, M. Augsburger, G. A. Beer, Stefano Bianco, A. M. Bragadireanu, Marco Bregant, W. H. Breunlich, Michael Cargnelli, D. Chatellard, J.-P. Egger, F. L. Fabbri, B. Gartner, C. Guaraldo, Ryugo S. Hayano, M. Iliescu, T. Ishiwatari, Takeyasu M. Ito, Masahiko Iwasaki, R. King, Paul E. Knowles, T. Koike, B. Lauss, V. Lucherini, Johann Marton, Edoardo Milotti, Françoise Mulhauser, Satoshi Nakamura, C. Petrascu, T. Ponta, Tai Sakuma, A. C. Sanderson, Lukas A. Schaller, L. Schellenberg, Hubert Schneuwly, Ryoichi Seki, Ryoichi Seki, D. Tomono, T. Yoneyama, E. Zavattini, J. Zmeskal 
31 Jan 2000-Nuclear Physics
4 Citations
The paper does not provide information about the composition of DEAR.
Open access•Book
Dear Yeats, Dear Pound, Dear Ford: Jeanne Robert Foster and Her Circle of Friends
Richard Londraville, Janis Londraville, William M. Murphy 
01 Jan 2001
1 Citations
The paper does not provide information about the composition of the word "dear." The paper is about the life of Jeanne Robert Foster and her circle of friends during the birth of the Age of Modernism.
Journal Article•DOI
The DEAR case
C. Guaraldo, Stefano Bianco, A. M. Bragadireanu, Fabrizio Fabbri, M. Iliescu, Takeyasu M. Ito, V. Lucherini, C. Petrascu, Marco Bregant, Edoardo Milotti, Andrea Vacchi, E. Zavattini, M. Augsburger, D. Chatellard, Paul E. Knowles, Françoise Mulhauser, Lukas A. Schaller, L. Schellenberg, Hubert Schneuwly, J.-P. Egger, D. Varidel, W. H. Breunlich, Michael Cargnelli, B. Gartner, R. King, B. Lauss, Johann Marton, J. Zmeskal, T. Ponta, Satoshi Nakamura, T. Koike, Ryugo S. Hayano, Masaki Hori, T. Ishikawa, T. Ishiwatari, Masahiko Iwasaki, Yoshinori Akaishi, G. A. Beer, A. C. Sanderson, Ryoichi Seki, Ryoichi Seki 
01 Jun 1999-Hyperfine Interactions
3 Citations
The paper does not provide information about the composition of DEAR.
Journal Article•DOI
Probabilistic DEAR models
Yanhong Cui, Renkuan G. Guo, Danni Guo 
01 Aug 2013-International Journal of Machine Learning and Cybernetics
3 Citations
The composition of DEAR models includes a random function with a linear difference equation-wise regression as the central tendency and a variance bound specified by Gaussian error analysis theory.

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