Disclosed are methods and structures of Multiple Kernel learning framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Advantageously, the disclosed methods and structures permit the use of binary classification technologies ...
Kernel target alignmentSoft margin SVMOne-class SVMThe two-stage multiple kernel learning (MKL) algorithms gained the popularity due to their simplicity and modularity. In this paper, we focus on two recently proposed two-stage MKL algorithms: ALIGNF...
Learning-to-Defer (L2D) facilitates optimal task allocation between AI systems and decision-makers. Despite its potential, we show that current two-stage L2D frameworks are highly vulnerable to adversarial attacks, which can misdirect queries or overwhelm decision agents, significantly degrading system ...
A binary classification framework for two-stage multiple kernel learning. Proc. of the 29th International Confer- ence on Machine Learning, 2012: 1295 - 1302.Kumar A, Niculescu-Mizil A, Kavukcuoglu K, Daume III H. A binary classification framework for two-stage multiple kernel learning. ...
论文地址:A Comprehensive Review On Two-Stage Object Detection Algorithms 目前目标检测领域的深度学习方法主要分为两类:two stage 的目标检测算法;one stage 的目标检测算法。前者是先由算法生成一系列作为样本的候选框,再通过卷积神经网络进行样本分类;后者则不用产生候选框,直接将目标边框定位的问题转化为回归问题...
Based on CMPNN, a framework combining two-stage Graph Attention Networks and Q-learning (TSGAT+Q-learning) is proposed in this paper. In the first stage, the agent embedding is completed, i.e., each service technician’s messages are represented by a constructed graph; In the second phase...
In this paper, we propose a two-stage distributed shared memory architecture (TSDSM). The lower bound of it is also given. Scheduling algorithms for a TSDSM imitating a FCFS output-queued (OQ) switch and a FIFO OQ switch are given too. The validities of these algorithms are theoretically...
Most essential works on Relation Extraction in the last decade were based on machine learning algorithms using a large number of hand-crafted features. Mainly, the top system of the DDIExtraction shared task [24] was a linear SVM classifier using a hybrid kernel with features based on syntactic...
The “apple-to-apple” comparison, which is exceedingly rare in the current research, shows that the two-stage post-processing method is able to outperform not just previously published results obtained through the kernel conditional density estimation, but also those from two other ELM-based ...
In the first stage, health states of the rolling bearing are automatically perceived by the FSFDPC, where no manual parameters need to be preset. In the second stage, MDDNN is constructed with parallel BLSTM and BGRU channels to extract the multi-dimensional feature maps from the input for ...