We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies ...
We present an analogy between the operation of a Wireless Sensor Network and the sampling and reconstruction of a signal. We measure the impact of three factors on the quality of ...
In this paper we examine master regression algorithms that leverage base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for c...
The Lanczos method is often used to solve a large and sparse symmetric matrix eigenvalue problem. There is a well-established convergence theory that produces bounds to predict the...