Uehara 1994] K. Uehara, M. Tanizawa, and S. Maekawa. PBL: Prototype-based learning algorithm. S. Wess, K.-D. Althoff, and M. M. Richter (eds). Topics in Case-Based Reasoning. Springer-Verlag, Berlin.Uehara, K., Tanizawa, M., Maekawa, S.: PBL: Prototype-based learning algorithm....
learning c-plus-plus opengl prototype cpp shaders graphics sdl2 graphics-programming rendering-engine clustering-algorithm rendering-pipeline 3d-graphics physically-based-rendering forward-plus-shading compute-shaders triangles deferred-shading image-based-lighting Updated May 29, 2022 C++ Angelo...
instance-based learning, pruning technologyAn algorithm is proposed to prune the prototype vectors (prototype selection) used in a nearest neighbor classifier so that a compact classifier can be obtained with similar or even better performance. The pruning procedure is error based; a prototype will ...
As another example, similar data may be grouped together (e.g., into the same cluster or class), and/or a machine learning model (such as a regression algorithm) may be used to smooth the raw training data 104. In some examples, the noisy data can be deleted manually. In some ...
Algorithm 2 Client local update Require:Client~c,~task~t,~global~communication~round~r,~global~prototypes~P Client~c,~task~t,~global~communication~round~r,~global~prototypes~P ; Output: Client~local~prototypes~Pct Client~local~prototypes~Ptc ; ^Dc,t ← Dc,t ∪ Mc ~//~Combine~task~...
Algorithm 1: Prototype-based Label Propagation. Input: X, Y , λ, α, nstep Init: c˜ k = 1 |S k| (xi,yi)∈S k xi,k = 0; while k < nstep do Estimating Assignment: Zi j = exp(−∥xi −c˜ j ∥2 2 ∥j′ exp(− xi...
点击蓝字关注我们文章来源:《高校辅导员学刊》2023年第3期基于K-prototype聚类的职业院校学生校园消费行为分析——以宣城职业技术学院为例作 者:庞波1,吴以兵2,汪青华3(宣城职业技术学院 1党委办公室;2学生处;3总务处)摘要:职业院校数...
3.5. Algorithm 在模型训练中,不仅采用交叉熵损失来约束每个训练样本尽可能接近相应的类原型,而且还设计了一个类间差异损失,使不同类的原型差异尽可能大。因此,我们将交叉熵损失和类间差损失之和作为最终的损失函数如下: 第一项是交叉熵损失,如下所示。
Based on the designed learning and update process, the proposed algorithm that can effectively avoid the inherent shortages of expensive running cost and noise sensitive about CNN is developed. Meanwhile, the proposed algorithm can be effectively applied to large-scale data reduction needs. PSNB ...
Experiments show that we outperform existing methods for weakly-supervised AVVP. We also show that learning with weak and iteratively re-estimated pseudo labels can be interpreted as an expectation-maximization (EM) algorithm providing further insight for our training procedure. PDF Abstract ...