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Weighted decomposition kernels

Sauro Menchetti, Fabrizio Costa, Paolo Frasconi
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
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?

Ravi (Ravinder), Kiran M.
2015 International Journal of Computer Applications  
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]

Qinghao Hu, Gang Li, Peisong Wang, Yifan Zhang, Jian Cheng
2018 Lecture Notes in Computer Science  
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]

Adam Woźnica, Alexandros Kalousis, Melanie Hilario
2010 Lecture Notes in Computer Science  
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

M. Pérez-Ortiz, P. A. Gutiérrez, C. Hervás-Martínez
2014 Proceedings of the International Conference on Neural Computation Theory and Applications  
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]

Shweta Jain, Yosuke Mizutani, Blair Sullivan
2022 arXiv   pre-print
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]

Yash Bhalgat, Yizhe Zhang, Jamie Lin, Fatih Porikli
2020 arXiv   pre-print
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

Sijmen De Jong, Cajo J. F. Ter Braak
1994 Journal of Chemometrics  
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

Fabrizio Costa, Sauro Menchetti, Alessio Ceroni, Andrea Passerini, Paolo Frasconi
2006 Conference of the European Chapter of the Association for Computational Linguistics  
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  
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

Ricardo Sousa, Mario-Dinis Ribeiro, Pedro Pimentel-Nunes, Miguel Tavares Coimbra
2013 Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems  
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

David R. Hardoon, John Shawe-Taylor
2009 Machine Learning  
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]

Richard S.J. Tol
2020 arXiv   pre-print
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

Taiyong Li, Min Zhou, Chaoqi Guo, Min Luo, Jiang Wu, Fan Pan, Quanyi Tao, Ting He
2016 Energies  
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]

Jakub Kośmider
2021 arXiv   pre-print
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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