Bright has built an automated system for ranking job candidates against job descriptions. The candidate's resume and social media profiles are interwoven to build an augmented user profile. Similarly, the job description is augmented by external databases and user-generated content to build an enhanced job profile. These augmented user and job profiles are then analyzed in order to develop numerical overlap features each with strong discriminating power, and in sum with maximal coverage. The resulting feature scores are then combined into a single Bright Score using a custom algorithm, where the feature weights are derived from a nation-wide and controlled study in which we collected a large sample of human judgments on real resume-job pairings. We demonstrate that the addition of social media profile data and external data improves the classification accuracy dramatically in terms of identifying the most qualified candidates.
, is wholly my own work unless otherwise referenced or acknowledged. The document has not been submitted for qualifications at any other academic institution.
This paper analyses effects of component mismatching and non-linearities in mixed analog-digital multipliers used in analog implementation of artificial neural networks. Function estimation and classification problems using a multilayer perceptron are studied, analyzing their results, accuracy requirements and network modifications required.
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