Cohort Analysis 
Cohort analysis is a subset of behavioral analytics that takes the data from a given eCommerce platform, web application, or online game and rather than looking at all users as one unit, it breaks them into related groups for analysis. These related groups, or cohorts, usually share common characteristics or experiences within a defined timespan. Cohort analysis allows a company to ‘see patterns clearly across the lifecycle of a customer (or user), rather than slicing across all customers blindly without accounting for the natural cycle that a customer undergoes.’ By seeing these patterns of time, a company can adapt and tailor its service to those specific cohorts. While cohort analysis is sometimes associated with a cohort study, they are different and should not be viewed as one in the same. Cohort analysis has come to describe specifically the analysis of cohorts in regards to big data and business analytics, while a cohort study is a more general umbrella term that describes a type of study in which data is broken down into similar groups. …
Tangent-Normal Adversarial Regularization
The ever-increasing size of modern datasets combined with the difficulty of obtaining label information has made semi-supervised learning of significant practical importance in modern machine learning applications. Compared with supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data. In order to utilize manifold information provided by unlabeled data, we propose a novel regularization called the tangent-normal adversarial regularization, which is composed by two parts. The two terms complement with each other and jointly enforce the smoothness along two different directions that are crucial for semi-supervised learning. One is applied along the tangent space of the data manifold, aiming to enforce local invariance of the classifier on the manifold, while the other is performed on the normal space orthogonal to the tangent space, intending to impose robustness on the classifier against the noise causing the observed data deviating from the underlying data manifold. Both of the two regularizers are achieved by the strategy of virtual adversarial training. Our method has achieved state-of-the-art performance on semi-supervised learning tasks on both artificial dataset and FashionMNIST dataset. …
KDSL
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet, we automatically construct a semantic knowledge base called DisDict, which provides refined feature words that highlight the differences among word senses, i.e., synsets. Second, we automatically generate new sense-labeled data by DisDict from unlabeled corpora. Third, these generated data, together with manually labeled data, are fed to a supervised learning neural network to model the semantic relations among synsets, feature words and their contexts. Jointly with the supervised learning process, we also implement unsupervised learning on unlabeled data as an auxiliary task. The experimental results show that KDSL outperforms several representative state-of-the-art methods on various major benchmarks. Interestingly, it performs relatively well even when manually labeled data is unavailable, thus provides a new promising backoff strategy for WSD. …
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