暂无分享,去创建一个
[1] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[2] Tsuyoshi Murata,et al. {m , 1934, ACML.
[3] Patrice Marcotte,et al. Some comments on Wolfe's ‘away step’ , 1986, Math. Program..
[4] D. Pollard. Convergence of stochastic processes , 1984 .
[5] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[6] Peter L. Bartlett,et al. Functional Gradient Techniques for Combining Hypotheses , 2000 .
[7] Robert E. Schapire,et al. Explaining AdaBoost , 2013, Empirical Inference.
[8] Tong Zhang,et al. Sequential greedy approximation for certain convex optimization problems , 2003, IEEE Trans. Inf. Theory.
[9] Andrew R. Barron,et al. Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.
[10] C. Darken,et al. Constructive Approximation Rates of Convex Approximation in Non-hilbert Spaces , 2022 .
[11] David Mease,et al. Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers , 2015, J. Mach. Learn. Res..
[12] Peter L. Bartlett,et al. Boosting Algorithms as Gradient Descent , 1999, NIPS.
[13] Martin Jaggi,et al. Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization , 2013, ICML.
[14] Peter L. Bartlett,et al. Efficient agnostic learning of neural networks with bounded fan-in , 1996, IEEE Trans. Inf. Theory.
[15] V. Koltchinskii,et al. Complexities of convex combinations and bounding the generalization error in classification , 2004, math/0405356.
[16] Stefano Ermon,et al. Boosted Generative Models , 2016, AAAI.
[17] John B. Shoven,et al. I , Edinburgh Medical and Surgical Journal.
[18] Stefan Wager,et al. Adaptive Concentration of Regression Trees, with Application to Random Forests , 2015, 1503.06388.
[19] Gunnar Rätsch,et al. Boosting Black Box Variational Inference , 2018, NeurIPS.
[20] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[21] Y. Makovoz. Random Approximants and Neural Networks , 1996 .
[22] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[23] Philip Wolfe,et al. An algorithm for quadratic programming , 1956 .
[24] Zhi-Hua Zhou,et al. On the doubt about margin explanation of boosting , 2010, Artif. Intell..
[25] Martin Jaggi,et al. On the Global Linear Convergence of Frank-Wolfe Optimization Variants , 2015, NIPS.
[26] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[27] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[28] Thomas Gärtner,et al. Greedy Feature Construction , 2016, NIPS.
[29] Shie Mannor,et al. Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity , 2003, J. Mach. Learn. Res..
[30] L. Jones. A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training , 1992 .
[31] Shahar Mendelson,et al. A Few Notes on Statistical Learning Theory , 2002, Machine Learning Summer School.
[32] Bernhard Schölkopf,et al. AdaGAN: Boosting Generative Models , 2017, NIPS.
[33] Peter L. Bartlett,et al. AdaBoost is Consistent , 2006, J. Mach. Learn. Res..
[34] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[35] Daijin Kim,et al. Robust Real-Time Face Detection Using Face Certainty Map , 2007, ICB.
[36] Francis R. Bach,et al. Breaking the Curse of Dimensionality with Convex Neural Networks , 2014, J. Mach. Learn. Res..
[37] Vladimir Koltchinskii,et al. Rademacher penalties and structural risk minimization , 2001, IEEE Trans. Inf. Theory.