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\Kepler has revolutionised our understanding of both exoplanets and theirhost stars. Asteroseismology is a valuable tool in the characterisation ofstars and \Kepler is an excellent observing facility to performasteroseismology. Here we select a sample of 35 \Kepler solar-type stars whichhost transiting exoplanets (or planet candidates) with detected solar-likeoscillations. Using available \Kepler short cadence data up to Quarter 16 wecreate power spectra optimised for asteroseismology of solar-type stars. Weidentify modes of oscillation and estimate mode frequencies by ``peak bagging''using a Bayesian MCMC framework. In addition, we expand the methodology ofquality assurance using a Bayesian unsupervised machine learning approach. Wereport the measured frequencies of the modes of oscillation for all 35 starsand frequency ratios commonly used in detailed asteroseismic modelling. Due tothe high correlations associated with frequency ratios we report the covariancematrix of all frequencies measured and frequency ratios calculated. Thesefrequencies, frequency ratios, and covariance matrices can be used to obtaintight constraint on the fundamental parameters of these planet-hosting stars.

Oscillation frequencies for 35 \Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning