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Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements. We present KeystoneML, a system that captures and optimizes the end-to-end large-scale machine learning applications for high-throughput training in a distributed environment with a high-level API. This approach...
In this paper, we present an out-of-sample extrapolation (OSE) scheme in the context of semi-supervised manifold learning (OSESSL). Manifold learning (ML) takes samples with high dimensionality and learns a set of low dimensional embeddings. Embeddings generated by ML preserve nonlinear relationships between samples allowing dataset visualization, classification, or evaluation of object similarity...
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