图 书 馆开课单位--弗劳恩霍夫智能分析与信息系统研究所
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1EEG Coupling, Granger Causality and Multivariate Autoregressive Models[脑电耦合、格兰杰因果关系及多元自回归模型]
Alois Schlögl(弗劳恩霍夫智能分析与信息系统研究所) EEG Coupling, Granger Causality and Multivariate Autoregressive Models
热度：22
Alois Schlögl(弗劳恩霍夫智能分析与信息系统研究所) EEG Coupling, Granger Causality and Multivariate Autoregressive Models
热度：22
2Large Scale Learning with String Kernels[使用字符串内核进行大规模学习]
Sören Sonnenburg(弗劳恩霍夫智能分析与信息系统研究所) In applications of bioinformatics and text processing, such as splice site recognition and spam detection, large amounts of training sequences are ava...
热度：42
Sören Sonnenburg(弗劳恩霍夫智能分析与信息系统研究所) In applications of bioinformatics and text processing, such as splice site recognition and spam detection, large amounts of training sequences are ava...
热度：42
3Inverse Methods for EEG and MEG Source Reconstruction[脑电和脑磁图源重建的逆方法]
Stefan Haufe; Guido Nolte(弗劳恩霍夫智能分析与信息系统研究所) In this lecture we review the most popular inverse methods for EEG and MEG source reconstruction. Inverse methods can be divided into three different ...
热度：83
Stefan Haufe; Guido Nolte(弗劳恩霍夫智能分析与信息系统研究所) In this lecture we review the most popular inverse methods for EEG and MEG source reconstruction. Inverse methods can be divided into three different ...
热度：83
4Machine Learning for Intrusion Detection[入侵检测的机器学习]
Pavel Laskov(弗劳恩霍夫智能分析与信息系统研究所) Intrusion detection is one of core technologies of computer security. The goal of intrusion detection goal is identi cation of malicious activity in a...
热度：63
Pavel Laskov(弗劳恩霍夫智能分析与信息系统研究所) Intrusion detection is one of core technologies of computer security. The goal of intrusion detection goal is identi cation of malicious activity in a...
热度：63
5A Dynamic HMM for Online Segmentation[在线动态HMM分割]
Jens Kohlmorgen(弗劳恩霍夫智能分析与信息系统研究所) We propose a novel method for the analysis of sequential data that exhibits an inherent mode switching. In particular, the data might be a non-station...
热度：72
Jens Kohlmorgen(弗劳恩霍夫智能分析与信息系统研究所) We propose a novel method for the analysis of sequential data that exhibits an inherent mode switching. In particular, the data might be a non-station...
热度：72
6Optimized Cutting Plane Algorithm for Support Vector Machines[优化的切削平面算法的支持向量机]
Vojtech Franc(弗劳恩霍夫智能分析与信息系统研究所) 我们已经开发了一种名为OCAS一个新的线性支持向量机（SVM）训练算法。其计算工作量呈线性变化关系的样本大小。在广泛的实证评价OCAS显著优于现有技术的SVM求解器...
热度：52
Vojtech Franc(弗劳恩霍夫智能分析与信息系统研究所) 我们已经开发了一种名为OCAS一个新的线性支持向量机（SVM）训练算法。其计算工作量呈线性变化关系的样本大小。在广泛的实证评价OCAS显著优于现有技术的SVM求解器...
热度：52
7Learning from Network Traffic: Computing Kernels over Connection Content[学习网络流量：计算内核连接的内容]
Pavel Laskov(弗劳恩霍夫智能分析与信息系统研究所)
热度：21
Pavel Laskov(弗劳恩霍夫智能分析与信息系统研究所)
热度：21
89Supervised Learning on Matrices with the Dual Spectral Regularization[具有双谱正则化的矩阵的监督学习]
Ryota Tomioka(弗劳恩霍夫智能分析与信息系统研究所)
热度：30
Ryota Tomioka(弗劳恩霍夫智能分析与信息系统研究所)
热度：30
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