Paper title: Pegasos: Primal Estimated sub-Gradient Solver for SVM 1. Scope and relevance: Relevance to the REALM project (1=borderline 5=spot on): 4 Contribution to the community (# citations, work extended by others?): 910 citations What ML methods are used? Support vector machine (mainly linear kernel, but there is a discussion of how to extend the works to nonlinear kernels) What is the problem class / use case? (anomaly detection, regression, classification etc): Classification What is the application domain? Text classification What type of data is studied? (time-series, steady-state, static/dynamic): Static 2. Quality and scientific soundness: Clarity of the presentation. structure, is the problem well defined? The paper is well written, and the problem is well defined. However, the paper assumes a reader has strong backgrounds in convex optimizations. Are the methods well described/referenced? Yes Are the experiments repeatable/extendable? Yes Are data sets publicly available? Yes Are alternative methods evaluated? Yes, SVM-Perf, a state-of-the-art SVM solver at the time (2007).