Fast Implementation of String-Kernel-Based Support Vector Classifiers by GPU Computing

2010 ◽  
pp. 143-151
Author(s):  
Yongquan Shi ◽  
Tao Ban ◽  
Shanqing Guo ◽  
Qiuliang Xu ◽  
Youki Kadobayashi
Keyword(s):  
Gpu Computing ◽  
Support Vector ◽  
String Kernel ◽  
Fast Implementation

STEM KERNELS FOR RNA SEQUENCE ANALYSES

2007 ◽  
Vol 05 (05) ◽  
pp. 1103-1122 ◽  
Author(s):  
YASUBUMI SAKAKIBARA ◽  
KRIS POPENDORF ◽  
NANA OGAWA ◽  
KIYOSHI ASAI ◽  
KENGO SATO
Keyword(s):  
Noncoding Rna ◽  
Natural Extension ◽  
Secondary Structures ◽  
Inner Product ◽  
Support Vector ◽  
String Kernel ◽  
Homology Detection ◽  
Genome Sequences ◽  
Rna Sequences ◽  
Functional Rna

Several computational methods based on stochastic context-free grammars have been developed for modeling and analyzing functional RNA sequences. These grammatical methods have succeeded in modeling typical secondary structures of RNA, and are used for structural alignment of RNA sequences. However, such stochastic models cannot sufficiently discriminate member sequences of an RNA family from nonmembers and hence detect noncoding RNA regions from genome sequences. A novel kernel function, stem kernel, for the discrimination and detection of functional RNA sequences using support vector machines (SVMs) is proposed. The stem kernel is a natural extension of the string kernel, specifically the all-subsequences kernel, and is tailored to measure the similarity of two RNA sequences from the viewpoint of secondary structures. The stem kernel examines all possible common base pairs and stem structures of arbitrary lengths, including pseudoknots between two RNA sequences, and calculates the inner product of common stem structure counts. An efficient algorithm is developed to calculate the stem kernels based on dynamic programming. The stem kernels are then applied to discriminate members of an RNA family from nonmembers using SVMs. The study indicates that the discrimination ability of the stem kernel is strong compared with conventional methods. Furthermore, the potential application of the stem kernel is demonstrated by the detection of remotely homologous RNA families in terms of secondary structures. This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel in order to find novel RNA families from genome sequences.

In silicotoxicity prediction by support vector machine and SMILES representation-based string kernel

2012 ◽  
Vol 23 (1-2) ◽  
pp. 141-153 ◽  
Author(s):  
D.-S. Cao ◽  
J.-C. Zhao ◽  
Y.-N. Yang ◽  
C.-X. Zhao ◽  
J. Yan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
String Kernel

scholarly journals Prediction of siRNA functionality using generalized string kernel and support vector machine

FEBS Letters ◽  
2005 ◽  
Vol 579 (13) ◽  
pp. 2878-2882 ◽  
Author(s):  
Reiji Teramoto ◽  
Mikio Aoki ◽  
Toru Kimura ◽  
Masaharu Kanaoka
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
String Kernel

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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