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Weighted decomposition kernels
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
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A weighted decomposition kernel (WDK) is computed by dividing objects into substructures indexed by a selector. ...
We introduce a family of kernels on discrete data structures within the general class of decomposition kernels. ...
Conclusions We introduced the weighted decomposition kernels, a computationally efficient and general family of kernels on decomposable objects. ...
doi:10.1145/1102351.1102425
dblp:conf/icml/MenchettiCF05
fatcat:ekyymodrnzfn3jmy4onjnhill4
Does there Exist Pruning Decomposition for MapReduce Expressions Arrangements?
2015
International Journal of Computer Applications
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How should we choose the weights 𝑤 𝛽 (c, X (n) ) that large weights are only assigned to kernel functions with low kernels? ...
Pruning Decomposition expressions with the same or similar weights are considered to be equally acceptable solutions and these weights directly depend on the objective or kernel function. ...
doi:10.5120/ijca2015906155
fatcat:eqipzzhvgzfsvobn4lw3i2265u
Training Binary Weight Networks via Semi-Binary Decomposition
[chapter]
2018
Lecture Notes in Computer Science
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Recently binary weight networks have attracted lots of attentions due to their high computational efficiency and small parameter size. ...
Besides, we propose an alternating optimization method to solve the semi-binary decomposition problem while keeping binary constraints. ...
Low-rank decomposition based methods Low-rank decomposition based methods [5, 6, 13] mainly use matrix or tensor decomposition methods to decompose convolutional kernels into several small matrices or ...
doi:10.1007/978-3-030-01261-8_39
fatcat:emdugucdnjh5vfo4l2blid427a
Adaptive Matching Based Kernels for Labelled Graphs
[chapter]
2010
Lecture Notes in Computer Science
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Most of them are based on the Cross Product (CP) Kernel applied on decompositions of graphs into sub-graphs of specific types. ...
Several kernels over labelled graphs have been proposed in the literature so far. ...
One surprising observation is that for NCA the decompositions into trees and long walks are assigned low weights. ...
doi:10.1007/978-3-642-13672-6_37
fatcat:3fbrtugmojd3tdbfrcrkz2mnlu
Learning Kernel Label Decompositions for Ordinal Classification Problems
2014
Proceedings of the International Conference on Neural Computation Theory and Applications
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The kernel parameters are optimised for each classification subtask in order to better adjust the kernel to the data. ...
More flexible multi-scale Gaussian kernels are considered to increase the goodness of fit of the kernel matrices. ...
Even taking into account that the last decomposition has been chosen to be the original learning problem, the weight of this kernel matrix is very low for some of the datasets (e.g. machine and eucalyptus ...
doi:10.5220/0005079302180225
dblp:conf/ijcci/Perez-OrtizGH14a
fatcat:xqt7xv2tcfg7hi5pexyitzu7gm
Faster Decomposition of Weighted Graphs into Cliques using Fisher's Inequality
[article]
2022
arXiv
pre-print
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Recently, Cooley et al. modeled this problem as Exact Weighted Clique Decomposition (EWCD) in which, given an edge-weighted graph G and a positive integer k, the goal is to decompose G into at most k ( ...
They show EWCD is fixed-parameter-tractable, giving a 4^k-kernel alongside a backtracking algorithm (together called cricca) to iteratively build a decomposition. ...
Thus, a natural question is: Does there exist a smaller kernel and/or a faster decomposition algorithm for the Exact Weighted Clique Decomposition problem? ...
arXiv:2206.07286v2
fatcat:4ivn5ht5pfec5jjo6efmam672i
Structured Convolutions for Efficient Neural Network Design
[article]
2020
arXiv
pre-print
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We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers. ...
complexity and fewer weights. ...
Figure 5 : Structural decomposition of a matrix multiplication.
Imposing Structure on Convolution Kernels To apply the structural decomposition, we need the weight tensors to be structured. ...
arXiv:2008.02454v2
fatcat:3l744vyejzbj5o42q6yjq3gd4e
Comments on the PLS kernel algorithm
1994
Journal of Chemometrics
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The rate-determining step in the kernel algorithm is the construction of the cross-product matrices XTX and XTY rather than their decomposition. ...
It closely follows the decomposition of cross-product matrices presented in Reference 6, leading to an elegant and concise reformulation of the kernel algorithm. ...
APPENDIX: SIMPLIFIED KERNEL ALGORITHM FOR PLS2 REGRESSION The following MATLAB* code starts from the cross-product matrices XtX (= XTX) and XtY (= XTY) and finds the first 'dim' components. ...
doi:10.1002/cem.1180080208
fatcat:hqki5gk5znc7pbkx6htg435ena
Decomposition Kernels for Natural Language Processing
2006
Conference of the European Chapter of the Association for Computational Linguistics
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We propose a simple solution to the sequence labeling problem based on an extension of weighted decomposition kernels. ...
We additionally introduce a multiinstance kernel approach for representing lexical word sense information. ...
Weighted Decomposition Kernels A weighted decomposition kernel (WDK) is characterized by the following decomposition structure: R = X , R, (δ, κ 1 , . . . , κ D ) where X = (S, Z 1 , . . . , Z D ), R(s ...
dblp:conf/eacl/CostaMCPF06
fatcat:2zgkr4s5rvb7fffzqcpu7scyzy
Kinetics of Pyrolysis of Date Kernels
2020
International Journal of Engineering and Advanced Technology
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The activation energy of the main decomposition step was determined and the values obtained using different kinetic models compared. The decomposition step simply followed first order kinetics. ...
The kinetics of pyrolysis of ground date kernels was investigated using thermogravimetric analysis in order to provide the necessary information for proper biochar formation and bioreactor design. ...
: Elementary analysis of date kernels [11]
Component
Weight %
C
47.2
H
6.8
N
2.9
S
0
O
37.9
ash
5.2
Table - III - : Peak temperatures for the main
decomposition step of date kernels ...
doi:10.35940/ijeat.c5419.029320
fatcat:mybxeqdqvjdydhbtitx4iby7om
Impact of SVM multiclass decomposition rules for recognition of cancer in gastroenterology images
2013
Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems
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It also outperformed standard decomposition rules when using a χ 2 kernel and attained competitive performances with a linear kernel. ...
We found that SDR coupled with a frequency term weight function attained the best overall results (80%) when trained with an intersection kernel. ...
Finally, in Table 3 it is presented the results for the χ 2 kernel. Although, this kernel choice improved the performance of standard decomposition rules for txx, it did not aid bxx. ...
doi:10.1109/cbms.2013.6627827
dblp:conf/cbms/SousaDPC13
fatcat:lexfzonfxjbatoipw4niquy6uu
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
2009
Machine Learning
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The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple sources into a joint kernel defined ...
Despite these uses, there have been no attempts made towards investigating the resulting tensor weight in respect to the contribution of the individual tensor sources. ...
TD weight maps In this section we visualise the weight maps in voxel space for the Tensor Decomposition (TD) (as detailed in Sect. 3). ...
doi:10.1007/s10994-009-5159-x
fatcat:cz6akucu6vec7bx2oyi5ptv6ny
Kernel density decomposition with an application to the social cost of carbon
[article]
2020
arXiv
pre-print
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Kernel decomposition can be applied in many other fields with discrete explanatory variables. ...
A kernel density is an aggregate of kernel functions, which are itself densities and could be kernel densities. This is used to decompose a kernel into its constituent parts. ...
I searched Scopus for "kernel decomposition", "composite kernel" and "kernel mixture".Cunningham and Williams (1994) show that a spectogram is the weighted sum of spectograms.Szymkowiak-Have et al. (2006 ...
arXiv:2003.09276v1
fatcat:ein7catcdng4vf3xozf4rvshwi
Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels
2016
Energies
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To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined ...
kernel of RVM. ...
function on x and x i , and w i is the weight of the kernel. ...
doi:10.3390/en9121014
fatcat:vnbdqexrsbbfzazcvprf25qq4q
The Wold-type decomposition for m-isometries
[article]
2021
arXiv
pre-print
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In particular, we introduce the k-kernel condition which we use to characterize analytic m-isometric operators which are unitarily equivalent to unilateral operator valued weighted shifts for m≥2. ...
The aim of this paper is to study the Wold-type decomposition in the class of m-isometries. ...
Observe that, since T satisfies the k-kernel condition, the kernel-range decomposition implies that T [n] g ∈ R(T ) for every g ∈ R(T ) and every n ∈ {1, . . . , k}. ...
arXiv:2006.15642v3
fatcat:uw5yexz6bjcobkciqplccq6lwq
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