它是机器学习和统计学中应用最广泛的模型之一，尽管名字中包含“回归”，但它本质上是一个分类模型线性组合特征：将输入与权重结合；概率转换：用Sigmoid函数输出概率；参数优化：通过极大似然估计找到最佳参数；决策边界划分：根据概率阈值（如0.5）分类。 扩展思考如何处理非线性可分数据？ （引入多项式特征或核方法）如何防止过拟合？ （正则化、交叉验证）逻辑回归能否用于多分类问题？ C#架构炼狱：从.NET 1.0到.NET 8的地狱级代码审查——用IL反编译+依 …

To improve the model accuracy of deep stochastic configuration network (DSCN) in multitarget robust parameter modeling tasks, this paper presents a multitarget robust DSCN modeling method. This method expands the hidden layer output matrix by optimizing the network model structure and exploiting the correlation between multiple targets to ...

To facilitate the discovery of novel tar-get (combinations), we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expres-sion profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data.

2024年6月6日 · 本文介绍了yolov10的改进版，独创可变形条带卷积(dscn)，作为轻量级dcnv3的替代方案，减少了计算量，提高了性能。 DSCN通过限制变形采样核在单轴上，降低了计算负荷。

2019年12月1日 · The proposed deep separable convolutional network (DSCN) directly uses the raw multi-sensor data as inputs, getting rid of the manual feature extraction and selection. The interrelationships of different sensor data are effectively modeled in the degradation behavior learning by introducing separable convolutions.

Abstract: In this paper, we present the deep sparse coding network (DSCN) - a novel deep learning framework that encodes intermediate representations with nonnegative sparse coding. DSCN is constructed from a cascade of bottleneck modules, each of which consists of two sparse coding layers with relatively wide and slim dictionaries that are ...

2021年3月1日 · 为了解决该问题，文章提出了一个双任务约束的孪生 卷积神经网络 （dual task constrained deep Siamese convolutional network, DTCDSCN）。 它由三个子网络构成：一个变化检测网络和两个语义分割网络（SSN）。 此外还引入了双注意力模块（dual attention module, DAM），以及改善了focus loss来抑制样本不均衡的问题。 建筑物变化检测算法主要可分为两类，一类是分类后比较法，另一类是直接分类法。 分类后比较法首先提取两张图像中的建筑 …

该篇论文提出的损失函数添加了频率用来解决数据不平衡（变化的像素点数量往往远小于未变化的像素点数量），在利用特征网络进行特征提取后添加了KNN和使用Transformer、CBAM都是为了更加精细的寻找变化点。..._dscn

2022年8月19日 · To facilitate the discovery of novel target (combinations), we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data.

2022年1月1日 · To solve this problem, we propose a Dense DSCN with a Hybrid Training mechanism (HT-DDSCN), which extends the network structure of the DSCN to a dense connection type and combines three typical optimisation techniques and one universal control strategy to optimise the calculation process of the output weights.