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Research on Density Prediction of Laser Powder Bed Fusion Process Parameters for IN718 Nickel-Based Superalloy Based on Machine Learning. [PDF]
Materials (Basel)Zhu L +5 more
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Novel field-based approaches reveal wheat genotypic differences in nitrogen use efficiency and grain protein dynamics. [PDF]
NPJ Sustain AgricSwarbreck SM +6 more
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Machine Learning and the Use of Spectroscopy for Adulteration Detection in Turmeric Powder. [PDF]
MoleculesKisalaei A +5 more
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Neural Processing Letters, 2021
This work proposes a new representation learning model called kernelized linear autoencoder. Instead of modeling non-linearity by the non-linear activation functions, we employ linear activations but account for non-linearity by the kernel trick. We propose four variants. The first one is the basic unsupervised kernelized linear autoencoder. The second
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This work proposes a new representation learning model called kernelized linear autoencoder. Instead of modeling non-linearity by the non-linear activation functions, we employ linear activations but account for non-linearity by the kernel trick. We propose four variants. The first one is the basic unsupervised kernelized linear autoencoder. The second
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Dimension of Kernels of Linear Operators
American Journal of Mathematics, 1992The basic question addressed in this paper is one of expressing the dimension of the intersection of kernels of linear operators that arise naturally in multivariate approximation theory in terms of the more easily computable dimensions of some basic blocks.
Jia, Rong-Qing +2 more
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2009 Ninth IEEE International Conference on Data Mining, 2009
The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays
Shohei Hido, Hisashi Kashima
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The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays
Shohei Hido, Hisashi Kashima
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Linear Replicator in Kernel Space
2010This paper presents a linear replicator [2][4] based on minimizing the reconstruction error [8][9] It can be used to study the learning behaviors of the kernel principal component analysis [10], the Hebbian algorithm for the principle component analysis (PCA) [8][9] and the iterative kernel PCA [3].
Wei-Chen Cheng, Cheng-Yuan Liou
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Regularized linear and kernel redundancy analysis
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yoshio Takane, Heungsun Hwang
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Kernel Density Estimation on a Linear Network
Scandinavian Journal of Statistics, 2016AbstractThis paper develops a statistically principled approach to kernel density estimation on a network of lines, such as a road network. Existing heuristic techniques are reviewed, and their weaknesses are identified. The correct analogue of the Gaussian kernel is the ‘heat kernel’, the occupation density of Brownian motion on the network.
Mcswiggan, G. +2 more
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