Accurate stellar properties are crucial to rigorously examine the relationship between host stellar environment and exoplanetary system properties. However, many tools designed to extract these properties require extensive computation time that can be inhibitive for large samples. To address this problem, we devise a new scheme that rapidly recovers stellar properties from Keck HIRES spectra by combining The Cannon, which uses supervised learning methods to extract stellar labels, with the Spectroscopic Properties of Cool Stars (SPOCS) catalog of over 1000 stars previously analyzed with the Spectroscopy Made Easy (SME). We demonstrate that our model recovers 18 labels — logg, Teff, vsini, and 15 elemental abundances (C, N, O, Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Fe, Ni, and Y) with uncertainties roughly equivalent to the discrepancy between different spectroscopic catalogues. We also show that, by interpolating our input spectra to a separate but overlapping wavelength range, we accurately recover stellar labels for archival Keck HIRES spectra, observed prior to the 2004 detector upgrade, that could not be analyzed uniformly together with the SPOCS catalog due to their more limited wavelength coverage. We conclude with prospects for future applications to inform statistical studies of exoplanet demographics.