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Generalization Guarantees for a Binary Classification Framework for Two-Stage Multiple Kernel Learning [article]

Purushottam Kar
2013 arXiv   pre-print
We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning. We also present bounds for sparse kernel learning formulations within the TS-MKL framework.  ...  Introduction Recently Kumar et al [6] proposed a framework for two-stage multiple kernel learning that combines the idea of target kernel alignment and the notion of a good kernel proposed in [1] to  ...  For any binary classification task over a domain X characterized by a distribution D over X × {±1}, a Mercer kernel K : X × X → R with associated Reproducing Kernel Hilbert Space H K and feature map Φ  ... 
arXiv:1302.0406v1 fatcat:qnpcyv2go5f5rkvgw6jjyfxinq

A Binary Classification Framework for Two-Stage Multiple Kernel Learning [article]

Abhishek Kumar, Alexandru Niculescu-Mizil, Hal Daume III
2012 arXiv   pre-print
In this paper we show that Multiple Kernel Learning can be framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel.  ...  In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels that is suitable for the task at hand has received significant attention from researchers  ...  In this paper we introduce TS-MKL, a general approach to Two-Stage Multiple Kernel Learning that encompasses the previous work based on target alignment as special cases.  ... 
arXiv:1206.6428v1 fatcat:pk2lgqnjereqzcmf5gr43dm6ve

Robust Head-Shoulder Detection Using a Two-Stage Cascade Framework

Ronghang Hu, Ruiping Wang, Shiguang Shan, Xilin Chen
2014 2014 22nd International Conference on Pattern Recognition  
In contrast, the second stage further boost the performance via multiple kernel learning on Riemannian manifold formed by Region Covariance Matrix (RCM), a second-order statistic descriptor with stronger  ...  In this paper, by exploiting the secondorder region covariance descriptor as a complement to widelyused histogram-based descriptors, we propose a new two-stage coarse-to-fine cascade framework to make  ...  CONCLUSION We have proposed a novel two-stage cascade framework for robust head-shoulder detection in this paper.  ... 
doi:10.1109/icpr.2014.482 dblp:conf/icpr/HuWSC14 fatcat:fr2fu2e4f5b6rledykaonfmpde

Multi-Task Multiple Kernel Relationship Learning [article]

Keerthiram Murugesan, Jaime Carbonell
2017 arXiv   pre-print
This paper presents a novel multitask multiple kernel learning framework that efficiently learns the kernel weights leveraging the relationship across multiple tasks.  ...  In order to tackle large-scale problems, we further propose a two-stage MK-MTRL online learning algorithm and show that it significantly reduces the computational time, and also achieves performance comparable  ...  We propose an efficient binary classification framework for learning the weights of these task-specific base kernels, based on target alignment [6] .  ... 
arXiv:1611.03427v2 fatcat:gpiorygjfjdd7dor7xf4zyamsy

Multi-Task Multiple Kernel Relationship Learning [chapter]

Keerthiram Murugesan, Jaime Carbonell
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
This paper presents a novel multitask multiple kernel learning framework that efficiently learns the kernel weights leveraging the relationship across multiple tasks.  ...  In order to tackle large-scale problems, we further propose a two-stage MK-MTRL online learning algorithm and show that it significantly reduces the computational time, and also achieves performance comparable  ...  Acknowledgements We thank the anonymous reviewers for their helpful comments.  ... 
doi:10.1137/1.9781611974973.77 dblp:conf/sdm/MurugesanC17 fatcat:ctdfk5sifvdkbfj3wnnhkme3di

RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs [article]

Chunlei Liu and Wenrui Ding and Xin Xia and Yuan Hu and Baochang Zhang and Jianzhuang Liu and Bohan Zhuang and Guodong Guo
2019 arXiv   pre-print
In this paper, we propose rectified binary convolutional networks (RBCNs), towards optimized BCNNs, by combining full-precision kernels and feature maps to rectify the binarization process in a unified  ...  framework.  ...  ., 2019] learns a set of diverse quantized kernels by exploiting multiple projections with discrete back propagation.  ... 
arXiv:1908.07748v2 fatcat:g2qkaqoeibhu7pw6q46sbs7eje

A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning [article]

Qiang Sun, Liting Wang, Maohui Li, Longtao Zhang, Yuxiang Yang
2019 arXiv   pre-print
In this paper, we propose a unified framework to predict the binary interestingness of images based on discriminant correlation analysis (DCA) and multiple kernel learning (MKL) techniques.  ...  interestingness cues, the SimpleMKL method is employed to enhance the generalization ability of the built model for the task of the binary interestingness classification.  ...  The fourth part is the binary interestingness classification with the multiple kernel learning methodology.  ... 
arXiv:1910.05996v1 fatcat:nmyfvccvxzg7njkplhirdliiuy

GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs [article]

Chunlei Liu, Wenrui Ding, Yuan Hu, Baochang Zhang, Jianzhuang Liu, Guodong Guo
2020 arXiv   pre-print
The reason lies in that the binarized kernels and activations of 1-bit DCNNs cause a significant accuracy loss and training inefficiency.  ...  in an end-to-end framework.  ...  And PCNN (Gu et al. 2019) learns a set of diverse quantized kernels by exploiting multiple projections with discrete back propagation.  ... 
arXiv:1911.11634v2 fatcat:k2arc53hyvd5bd66dcyafptzd4

Connectionist-Symbolic Machine Intelligence using Cellular Automata based Reservoir-Hyperdimensional Computing [article]

Ozgur Yilmaz
2015 arXiv   pre-print
It is possible to estimate the kernel for linear cellular automata via metric learning, that enables a much more efficient distance computation in support vector machine framework.  ...  Also, binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way for concept building and symbolic processing.  ...  The classification performance for real and binary neural representation is given for linear and RBF kernels.  ... 
arXiv:1503.00851v3 fatcat:b4mbomhexjezfitvhsny4yjkga

A Multiclass Classification Framework for Document Categorization [chapter]

Qi Qiang, Qinming He
2006 Lecture Notes in Computer Science  
use of the kernel trick for "weak" algorithms to work in high dimensional spaces, finally improves the performances of text classification.  ...  As we known, text classification problem is representative multiclass problem. This paper describes a framework, which we call Strong-to-Weakto-Strong (SWS).  ...  In KPCA, kernel serves as preprocessing while in SVM kernel has effect on classification in the middle of process. There could be two stages for kernel to affect the result of our algorithm.  ... 
doi:10.1007/11669487_42 fatcat:mg4aasxtgnfkzdwy226xjtykmq

Visual-Saliency-Based Abnormality Detection for MRI Brain Images—Alzheimer's Disease Analysis

A. Diana Andrushia, K. Martin Sagayam, Hien Dang, Marc Pomplun, Lien Quach
2021 Applied Sciences  
Multiple-kernel learning (MKL) and simple and efficient MKL (SEMKL) were used to classify Alzheimer's disease from normal controls.  ...  This study explores the role of a visual saliency map in the classification of Alzheimer's disease (AD).  ...  Acknowledgments: We would like to thank all of our universities for facilitating our time support in this study. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11199199 fatcat:pdi4dpt4hrgkzluxdmvalgufo4

Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of Compounds [article]

Lingwei Xie, Song He, Shu Yang, Boyuan Feng, Kun Wan, Zhongnan Zhang, Xiaochen Bo, Yufei Ding
2018 arXiv   pre-print
In this paper, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains.  ...  The framework utilizes the adversarial strategy to effectively learn target representations and models their nonlinear dependency.  ...  Multi-Class Classification Figure 2 : The training for the whole framework consists of two stages.  ... 
arXiv:1810.00867v1 fatcat:ysjmsdkndfe7rapwbznwngvxgm

Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert-Schmidt Independence Criterion

Tinghua Wang, Jie Lu, Guangquan Zhang
2018 IEEE transactions on fuzzy systems  
Multiple kernel learning (MKL) is a principled approach to kernel combination and selection for a variety of learning tasks such as classification, clustering and dimensionality reduction.  ...  We propose a new fuzzy membership function using a heuristic strategy based on the HSIC. The proposed HSIC-FMKL is a two-stage kernel learning approach and the HSIC is applied in both stages.  ...  THE TWO-STAGE FUZZY MKL METHOD (HSIC-FMKL) In this section, we present the two-stage fuzzy MKL method (HSIC-FMKL) for learning kernels in detail.  ... 
doi:10.1109/tfuzz.2018.2848224 fatcat:lac6lcnkdvhjrb6scn7e4krmi4

Trainable Convolution Filters and Their Application to Face Recognition

Ritwik Kumar, Arunava Banerjee, Baba C. Vemuri, Hanspeter Pfister
2012 IEEE Transactions on Pattern Analysis and Machine Intelligence  
kernel orders and shapes for each patch, a boosting scheme for automatically selecting the best weighted combination of the classifiers to achieve higher per-patch classification rate. 3) A scheme for  ...  aggregating the classification information obtained for each patch via voting for the parent image classification.  ...  ACKNOWLEDGMENTS A preliminary version of this work appeared in [24] . This work was in part supported by US National Science Foundation (NSF) Grant No. PHY-0835713 to Hanspeter Pfister.  ... 
doi:10.1109/tpami.2011.225 pmid:22144519 fatcat:4bh2awuxgzc5ppbj5f2a342ohu

Modeling social strength in social media community via kernel-based learning

Jinfeng Zhuang, Tao Mei, Steven C.H. Hoi, Xian-Sheng Hua, Shipeng Li
2011 Proceedings of the 19th ACM international conference on Multimedia - MM '11  
In this paper, we present a kernel-based learning to rank framework for inferring the social strength of Flickr users, which involves two learning stages.  ...  The first stage employs a kernel target alignment algorithm to integrate the heterogeneous data into a holistic similarity space.  ...  . • We propose a novel two-stage kernel-based learning framework for social strength modeling, which effec-tively integrates heterogeneous data by optimally combining multiple kernels, and learns to rank  ... 
doi:10.1145/2072298.2072315 dblp:conf/mm/ZhuangMHHL11 fatcat:wdv7y5k4czhwveqkhxlr6f5yta
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