… We also evaluate our unsupervised-CNN without finetuning for scene classification task
on MIT Indoor 67 [36]. We train a linear classifier using softmax loss. Using pool5 features …

… EVALUATIONS We evaluate the online detection results of SCA using two datasets … Table
1 shows the evaluation results in terms of False Positive Rate (FPR) and True Positive Rate (…

… Therefore, in contrast to the common practice of avoiding parameters in evaluation indexes
for unsupervised learning, we advocate here that for outlier detection, it is nonetheless …

… EVALUATION: The major part of the algorithm covers the evaluation indices used for evaluating
… used the External Evaluation Index Calinski-Harabasz for the Evaluation of the optimal …

… supervised learning with unsupervised learning are … of the supervised model to evaluate the
importance of sentences. The … In this section, we evaluate the effectiveness of our proposed …

… , rigid, and very particular evaluation perspective. Therefore, in contrast to the common
practice of avoiding any parameters in evaluation indexes for unsupervised learning, here we …

… In this study, the importance of unsupervised learning techniques in the existing typical …
Second, we optimize the ensemble learning quality control protocol (ELQCP) by evaluating …

… approach is that we do not evaluate combinations of more than d features within the
dependence structure. However, according to our experiments, this is enough in most of the cases. …

… (MDL) analysis to model unsupervised learning of the morphological … the notion of evaluation
metric in early generative grammar. … It performs unsupervised learning in the sense that the …

… supervised machine learning methods to evaluate the mix of facies across the dataset.
These distributions are also used later to map clusters identified using unsupervised learning to …