In a supervised learning setting, humans are required to annotate a large amount of dataset manually. Then, models use this data to learn complex underlying relationships between the data and label and develop the capability to predict the label, given the data. Deep learning models are generally...
Then, we compute the entropy of user–user connections given user opinions in order to map user interactions to a quantifiable value that expresses the uncertainty contributed to the network by each user. Further, by exploiting the principle of entropy, we select users based on a threshold ...
Specifically, we use Latent Dirichlet Allocation (LDA) to reduce the feature dimension of document vectors to a lower dimension of topic vectors. Then the procedure of discovering relevant documents using a PU learning method is conducted in the topic space. Using Mean Average Precision (MAP) and...
本文是CVPR2019 的文章,作者给出了一个很强的人体ReID baseline模型,文中包含了许多ReID训练技巧并提出一种BNNeck,在Market1500和DukeMTMC-ReID 两个数据集上达到了目前最好的Rank-1 精度和mAP。 论文链接:《Bag of Tricks and A Strong Baseline for Deep Person Re-identificatio...论文...
Clustering unlabeled data with SOMs improves classification of labeled real-world data We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to ... R Dara,SC Kremer,DA Stacey - IEEE 被引量: ...
This means that they look for functions that map all negative examples to zero. Given a class prior, the minimal proportion from the negative distribution that is selected by any function is estimated. The class prior is the largest value for which that proportion is below a given threshold (...
Plot alcohol consumption on a map using Plotly to visualize the geographic data. Create more visualizations using Seaborn to understand trends over time. Use Plotly Animation to create an interactive dashboard for stakeholders. Use the elbow method to determine the optimal number of clusters for K-...
During the shrink phase, we use the truth dataset to map the resulting clusters to a specific class label (the possibility of many clusters being mapped to a single class label). With the expand step, we moved from the critical assumption of the cluster to class mapping approaches such that...