Training and Testing Low-degree Polynomial Data Mappings via Linear SVM

By Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin. 2010

590 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017, methodology): “Besides generalized linear models and FM, a few other models are proposed for CTR prediction, such as tree-based model [ He et al. , 2014 ] , tensor based model [ Rendle and Schmidt-Thieme, 2010 ] , support vector machine [ Chang et al. , 2010 ] , and bayesian model [ Graepel et al. , 2010 ] .”
Online Learning: A Comprehensive Survey (2018, methodology): “…integrate some existing online learning algorithms (e.g., OGD) with kernel approximation techniques (Lin and Chen, 2011; Sonnenburg and Franc, 2010; Chang et al., 2010) to derive scalable online kernel learning algorithms, including Fourier Online Gradient Descent (FOGD) that explores random…”
Explicit Approximations of the Gaussian Kernel (2011, background): “The use of explicit monomial features of the form of (15) has been discussed recently as a way of speeding up training with the polynomial kernel [Sonnenburg and Franc, 2010, Chang et al., 2010].”
Is Retriever Merely an Approximator of Reader? (2020, methodology): “…corresponds well with the motivation for the kernel method in SVMs (Cortes & Vapnik, 1995), where one would need an inﬁnite number of dimensions for the feature map (two-tower) to exactly mimic even a simple kernel (one-tower) such as an RBF kernel (Chang et al., 2010) in inner product space.”
Entity Disambiguation with Web Links (2015, methodology): “To capture non-linear feature relationships we incorporate a degree-2 polynomial kernel via explicit feature mapping (Chang et al., 2010).”
Memory Efficient Kernel Approximation (2014, methodology): “Chang et al. (2010) propose to use the kernel expansion for low-degree polynomial kernels.”
Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training (2012, methodology): “…was performed. c©2012 Zhuang Wang, Koby Crammer and Slobodan Vucetic. scale learning resulted in many algorithms for training SVMs (Cortes and Vapnik, 1995) using large data (Vishwanathan et al., 2003; Zhang, 2004; Bordes et al., 2005; Tsang et al., 2005; Joachims, 2006; Hsieh et al., 2008;…”
Recent Advances of Large-Scale Linear Classification (2012, background): “Chang et al. [9] analyze when this approach leads to faster training and testing.”
On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent (2016, background): “The datasets [24] range from small to large scale.”
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection (2016, methodology): “The Radial basis function (RBF) [29] kernel was also applied.”
AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction (2020, methodology): “Models based on quadratic polynomial mappings [3] and factorization machines [14, 29] learn 2nd-order feature interactions while Higher-Order Factorization Machine (HOFM) [2] approximates high-order feature interactions through the ANOVA kernel.”
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising (2018, methodology): “Many models have been proposed to resolve this problem such as Logistic Regression (LR) [4, 16, 21], Polynomial-2 (Poly2) [3], treebased models [11], tensor-based models [20], Bayesian models [8], and Field-aware Factorization Machines (FFMs) [12, 13].”
Field-aware Factorization Machines for CTR Prediction (2016, background): “al [4] have shown that a degree-2 polynomial mapping can often effectively capture the information of feature conjunctions.”
A Survey of Model Architectures in Information Retrieval (2025, unknown): “…have explored different backbone neural network architectures in this era, including Convolutional Neural Network (CNN, LeCun et al., 1989, 1998), Long Short Term Memory (LSTM, Hochreiter and Schmidhuber, 1997) and kernel methods (Vert et al., 2004; Chang et al., 2010; Xiong et al., 2017).”