Automated selection of interaction effects in sparse kernel methods to predict pregnancy viability

AbstractThe primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns that conventional linear regression models cannot. Furthermore, these methods are also powerful for leveraging environmental covariates, such as genotype × environment (G×E) prediction, among others. In this study we provide the building process of seven kernel methods: linear, polynomial, sigmoid, Gaussian, Exponential, Arc-cosine 1 and Arc-cosine L. Additionally, we highlight illustrative examples for implementing exact kernel methods for genomic prediction under a single-environment, a multi-environment and multi-trait framework, as well as for the implementation of sparse kernel methods under a multi-environment framework. These examples are followed by a discussion on the strengths and limitations of kernel methods and, subsequently by conclusions about the main contributions of this paper.

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Sparse kernel methods

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)34857-7 ◽

2003 ◽

Vol 36 (16) ◽

pp. 795-800

Author(s):

Steve R. Gunn

Keyword(s):

Kernel Methods ◽

Sparse Kernel Methods ◽

Sparse Kernel

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Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization

Computational Intelligence and Neuroscience ◽

10.1155/2016/2305854 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Chunyuan Zhang ◽

Qingxin Zhu ◽

Xinzheng Niu

Keyword(s):

Online Learning ◽

Least Squares ◽

Computational Cost ◽

Recursive Least Squares ◽

Learning Problems ◽

Temporal Difference ◽

Sparse Kernel Methods ◽

Feature Dictionary ◽

Window Approach ◽

Sparse Kernel

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii)L2andL1regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can makeL1regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms.

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Kernel methods and haplotypes used in selection of sparse DNA markers for protein yield in dairy cattle

Mathematical Biosciences ◽

10.1016/j.mbs.2013.01.009 ◽

2013 ◽

Vol 243 (1) ◽

pp. 57-66 ◽

Cited By ~ 1

Author(s):

Sylvia Young ◽

Michael E. Goddard ◽

Jennie E. Pryce ◽

Guang Deng

Keyword(s):

Dairy Cattle ◽

Kernel Methods ◽

Dna Markers ◽

Protein Yield ◽

Selection Of

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Research on Manual Scheduling Technology Using the Pattern of Human-Computer Cooperation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.472-475.3228 ◽

2012 ◽

Vol 472-475 ◽

pp. 3228-3235

Author(s):

Jing Sheng Li ◽

Ai Min Wang ◽

Cheng Tong Tang ◽

Zhi Bing Lu

Keyword(s):

Kernel Methods ◽

Job Shop ◽

Auxiliary Function ◽

Gantt Chart ◽

Manual Adjustment ◽

Automatic Scheduling ◽

Selection Of

The automatic scheduling is not suitable for manufacture in most case, because it cannot fully utilize the experience of the operator and effectively response the disturbance events from the production progress. In order to solve these problems the manual scheduling technology using the pattern of human-computer collaborative is proposed. The graphical scheduling block has been discrete through applying the kernel methods including the combination of multi-layer displaying of Gantt-Chart, the model of machine calendar, mouse selected mechanism and auxiliary function for manual scheduling. By building multi-layer normative modular manual scheduling operations, we divide the manual scheduling operation into three steps: the selection of manual scheduling process, the selection of the inserting base, and the inserting mode. At the same time constrains transferring mode provoked by the three kinds of inserting mode is analyzed. By controlling constrains in the progress of the manual adjustment makes sure that the manual scheduling result meets the demand of job shop production. Finally, the algorithms in this paper are validated by the software developed with C++.

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Selection of a Model for a Two-Factor Analysis of Variance

Psychological Reports ◽

10.2466/pr0.1966.19.3f.1319 ◽

1966 ◽

Vol 19 (3_suppl) ◽

pp. 1319-1332 ◽

Cited By ~ 1

Author(s):

Leila S. Cain

Keyword(s):

Factor Analysis ◽

Empirical Evidence ◽

Analysis Of Variance ◽

Significant Interaction ◽

Interaction Effects ◽

Variance Model ◽

Random Samples ◽

Fixed Model ◽

Main Effects ◽

Selection Of

There are three models for a two-factor analysis of variance, Model I (effects fixed), Model II (effects random) and Model III (mixed). In Model I main effects and interaction effects may always be estimated, but the results of the analysis may not be generalized to any effects other than those represented in the study. If there is a significant interaction in Model II, neither main effects nor interaction effects may be meaningfully estimated, but the results of the analysis may be generalized to the populations of which the main effects are random samples. Empirical evidence suggests application of Model I procedures to Model II data can produce results comparable to those obtained by “proper” usage of Model I methods.

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Cognitive gadgets and cognitive priors

Behavioral and Brain Sciences ◽

10.1017/s0140525x19000992 ◽

2019 ◽

Vol 42 ◽

Author(s):

Gian Domenico Iannetti ◽

Giorgio Vallortigara

Keyword(s):

Cognitive Neuroscience ◽

Selection Of

Abstract Some of the foundations of Heyes’ radical reasoning seem to be based on a fractional selection of available evidence. Using an ethological perspective, we argue against Heyes’ rapid dismissal of innate cognitive instincts. Heyes’ use of fMRI studies of literacy to claim that culture assembles pieces of mental technology seems an example of incorrect reverse inferences and overlap theories pervasive in cognitive neuroscience.

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Note on Selection of Coordinate Systems for Geodynamics

International Astronomical Union Colloquium ◽

10.1017/s0252921100051174 ◽

1975 ◽

Vol 26 ◽

pp. 395-407

Author(s):

S. Henriksen

Keyword(s):

Sea Level ◽

The Other ◽

Coordinate Systems ◽

Mean Sea Level ◽

The Earth ◽

The One ◽

Static Systems ◽

Selection Of

The first question to be answered, in seeking coordinate systems for geodynamics, is: what is geodynamics? The answer is, of course, that geodynamics is that part of geophysics which is concerned with movements of the Earth, as opposed to geostatics which is the physics of the stationary Earth. But as far as we know, there is no stationary Earth – epur sic monere. So geodynamics is actually coextensive with geophysics, and coordinate systems suitable for the one should be suitable for the other. At the present time, there are not many coordinate systems, if any, that can be identified with a static Earth. Certainly the only coordinate of aeronomic (atmospheric) interest is the height, and this is usually either as geodynamic height or as pressure. In oceanology, the most important coordinate is depth, and this, like heights in the atmosphere, is expressed as metric depth from mean sea level, as geodynamic depth, or as pressure. Only for the earth do we find “static” systems in use, ana even here there is real question as to whether the systems are dynamic or static. So it would seem that our answer to the question, of what kind, of coordinate systems are we seeking, must be that we are looking for the same systems as are used in geophysics, and these systems are dynamic in nature already – that is, their definition involvestime.

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Measurements of the Cape Astrometric Survey of the Southern Hemisphere

International Astronomical Union Colloquium ◽

10.1017/s0252921100074534 ◽

1978 ◽

Vol 48 ◽

pp. 515-521

Author(s):

W. Nicholson

Keyword(s):

Southern Hemisphere ◽

Automatic Measuring ◽

Measuring Machine ◽

Final Selection ◽

Selection Of

SummaryA routine has been developed for the processing of the 5820 plates of the survey. The plates are measured on the automatic measuring machine, GALAXY, and the measures are subsequently processed by computer, to edit and then refer them to the SAO catalogue. A start has been made on measuring the plates, but the final selection of stars to be made is still a matter for discussion.

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