Sentence examples for predict attributes from inspiring English sources
Specifically, we unify the attribute relation learning and the attribute classifier design into a common objective function, through which we can not only predict attributes, but also automatically discover the relation between attributes from data.
In this work, we consider embeddings for social media users and demonstrate that these can be used to identify users who behave similarly or to predict attributes of users.
Currently, kernel methods are popular tools in QSAR modeling and are used to predict attributes such as activity towards a therapeutic target, ADMET properties (absorption, distribution, metabolism, excretion, and toxic effects), and adverse drug reactions.
The prediction APIs focus on predicting attributes based on other attributes.
We use this model to demonstrate how homophily-based methods for predicting attributes on social networks based on friends (that is, 'the company you keep') are fundamentally different from monophily-based methods based on friends-of-friends (that is, 'the company you're kept in').
The inverse problem was formulated in a nonlinear optimization framework in which model parameters were estimated by minimizing a complex penalty function, representing the discrepancies between the observed and predicted attributes of the physical system.
For predicting attributes such as weight, our method relies on vessel type classification.
The predictive ranking API is related to this, and will let users rank large numbers of objects based on predicted attributes.
SVM classifier employs the generic representation learnt for vessel type classification, whereas CNN employs a representation specifically learnt for predicting attributes.
When we weight queries by the predicted attributes, the Simple w/ Block method further improves the Simple method by 5.0%% in terms of (F_1) measure without increasing the number of issued queries.
The % Perfect column shows the percentage of reactions whose predicted attributes match the standard exactly.
