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A Comparative Analysis of Extreme Gradient Boosting, Decision Tree, Support Vector Machines, and Random Forest Algorithm in Data Analysis of College Students' Psychological Health

Qiong Li, Jian Zhou
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Liquid Crystalline Elastomers in Soft Robotics: Assessing Promise and Limitations

Advanced Robotics Research, EarlyView.
Liquid crystalline elastomers (LCEs) are programmable soft materials that undergo large, anisotropic deformation in response to external stimuli. Their molecular alignment encodes directional actuation in a monolithic structure, making them long‐standing candidates for soft robotic systems.
Justin M. Speregen, Timothy J. White
wiley   +1 more source

Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine

Joang Ipmawati, Saifulloh Saifulloh, Kusnawi Kusnawi   +2 more
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Significance of Support Vector Machine Classifier for Predicting Defects in Software Development

Abdulrazak Muhammad Gatawa, Yahaya Isah Shehu   +1 more
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APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA

Felice Elena, Ridwan Dwi Irawan, Benny Yong   +2 more
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Evaluation of Lower-Limb Brunnstrom Recovery Stage via High-Density Plantar Pressure and Global Fuzzy Granular Support Vector Machine

Qiangqiang Chen, Xiaoyu Chen, Linjie He, Taiyang Liu, Lingyu Liu, Lingjing Jin, Chen Chen, Bin Yin, Wei Chen, Wenting Qin, Hongyu Chen   +10 more
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Field support vector machines

Proceedings of the 1st International Conference on Internet of Things and Machine Learning, 2017
Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d.
Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang   +2 more
openaire   +1 more source

Distributed Support Vector Machines

IEEE Transactions on Neural Networks, 2006
A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes).
A, Navia-Vazquez, D, Gutierrez-Gonzalez, E, Parrado-Hernandez, J J, Navarro-Abellan   +3 more
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Fuzzy support vector machines

IEEE Transactions on Neural Networks, 2002
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions ...
Lin, Chun-Fu, Wang, Sheng-De
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Arbitrary Norm Support Vector Machines

Neural Computation, 2009
Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L∞-norm SVM, are rarely seen in the literature.
Huang, Kaizhu, Zheng, Danian, King, Irwin, Lyu, Michael R.   +3 more
openaire   +3 more sources