The main idea of this work is to directly embody the Hamming radius into the loss functions, leading to Maximum-Margin Hamming Hashing (MMHH), a new model specifically optimized for Hamming space retrieval. We introduce a max-margin t-distribution loss, where the t-distribution concentrates more...
We also consider soft-margin learning, where we minimize a trade-off between the trace norm of X and its hinge-loss relative to YS : minimize X Σ +c ia∈S max(0, 1 ? Yia Xia ). (1) As in maximum-margin linear discrimination, there is an inverse dependence between the norm and ...
Maximum-Margin Matrix Factorization Nathan Srebro Dept.of Computer Sci ence University of Toronto Toronto(ON)CANA DA nati@cs.toronto.edu ..
q属于{1,2}表示L1,L2惩罚,wTfi(yi)计算的是累积奖励,fi(yi)表示的是期望特征计数,Li(y)表示的是损失,即预测行为和示例行为的差距,加入该正则项后,示例策略和其它策略不仅要在奖励上相近,行为上也要相近。
Fast maximum margin matrix factorization for collaborative prediction Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approxima... JDM Rennie,N Srebro - Machine Learning, Twenty-second International ...
到此为止,算是完成了 Maximum Margin Classifier 的介绍,通过最大化 margin ,我们使得该分类器对数据进行分类时具有了最大的 confidence (实际上,根据我们说给的一个数据集的 margin 的定义,准确的说,应该是“对最不 confidence 的数据具有了最大的 confidence”——虽然有点拗口)。不过,到现在似乎还没有一点点...
的值是不会改变的,它只随着 hyper plane 的变动而变动,因此,这是更加合适的一个 margin 。这样一来,我们的 maximum margin classifier 的目标函数即定义为 当然,还需要满足一些条件,根据 margin 的定义,我们有 其中 ,根据我们刚才的讨论,即使在超平面固定的情况下, ...
If xn is the closest point to the hyperplane, the maximum margin is given by: \newcommand \abs[1]{\lvert#1\rvert} \newcommand \modd[1]{\lVert#1\rVert} \frac{\abs {y(x_n)} }{\modd{w}} = \frac{t_n y(x_n)}{\modd{w}} = \frac{t_n \left(w^T\phi\left(x_n\right...
It also shares most beneficial properties with single-label maximum-margin approaches, in particular formulation as a convex optimization problem, efficient working set training, and PAC-Bayesian generalization bounds. 1 展开 会议名称: Neural Information Processing Systems ...
In this paper, we have proposed a maximum margin semi-supervised model, named 3C-SVM, to learn from labeled and mixed unlabeled data. In order to alleviate the effect of mixed unlabeled data, we build up the formulation based on the logistic principle and maximum entropy principle. More speci...