Supervised learningThis paper deals with the machine learning model as a framework of regularized loss minimization problem in order to obtain a generalized model. Recently, some studies have proved the success and the efficiency of nonsmooth loss function for supervised learning problems Lyaqini et ...
上节课主要介绍了线性支持向量机(Linear Support Vector Machine)。Linear SVM的目标是找出最“胖”的分割线进行正负类的分离,方法是使用二次规划来求出分类线。本节课将从另一个方面入手,研究对偶支持向量机(Dual Support Vector Machine),尝试从新的角度计算得出分类线,推广SVM的应用范围。 1. Motivation of Dual...
The SDCA chooses a different approach that optimizes the dual problem instead. The dual loss function is parametrized by per-example weights. In each iteration, when a training example from the training data set is read, the corresponding example weight is adjusted so that the dual loss ...
Our guarantees for both methods are expressed in terms of the original nonsmooth primal problem based on the hinge-loss.doi:10.48550/arXiv.1303.2314Takáč, MartinBijral, AvleenRichtárik, PeterSrebro, NathanTaka´cˇ, Martin, Bijral, Avleen, Richta´rik, Peter, and Srebro, Nathan. Mini-...
Since assigning division-type is a classification problem, we employed a machine learning approach38. A K-means clustering algorithm (k = 3 for symmetric self-renewing with two stem daughter cells, symmetric committed with two committed daughter cells, and asymmetric divisions) coupled to a 1...
Convex sparsity-inducing regularizations are ubiquitous in high-dimension machine learning, but their non-differentiability requires the use of iterative solvers. To accelerate such solvers, state-of-the-art approaches consist in reducing the size of the optimization problem at hand. In the context of...
Neural machine translation has achieved state-of-art performance with sufficient data, but it still suffers from the data scarcity problem for low-resource language pairs. Teacher-student model that transfers knowledge from a pivot→targ... Y Cai,M Zhu - 《Journal of Physics Conference》 被引量...
Secondly, we use a joint measurement method for the spatial-temporal information to fully harness the data, and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution problem. The experimental results in...
For the ACNet, we postulated that finding the optimal WD is a problem with a correct answer (ground truth), which can be solved using a supervised learning–based deep learning model. Therefore, the ACNet was trained using the mean square error loss calculated by comparing the output and the...
[31], we take the logarithm and use \((\ln \varDelta , \ln k_{\text {T}}, \ln z, \ln m^2, \ln \varDelta p_{\text {T}})\) as the interaction features for each particle pair to avoid the long tail problem. Moreover, apart from the 5 interaction features, we design ...