A brain-computer interface (BCI) provides a direct control pathway between human brain and external devices. Steady-state visual evoked potential based BCI (SSVEP-BCI) has been proven to be a valuable solution due to its advantages of high information transfer rate (ITR) and minimal calibration requirement. Recently, some methods have been proposed based on calibration-training techniques to compute optimal spatial filters from covariances, and have achieved good detection performance. However, these methods ignore the temporally-varying and spatially-coupled characteristics of the EEG signals, which is essentially an important clue for enhancing ITR. More importantly, existing methods cannot well deal with intrinsic noise components of electroencephalogram (EEG) signals, greatly affecting their detection performance. In this paper, we propose a novel method, termed as Sum of Similarity-Regularized Squared Correlations (SSRSC), which is extended and regularized from the sum of squared correlations. We simultaneously compute the squared correlations for both calibration data and sine-cosine harmonics templates, and mitigate variations by the similarity regularization. Moreover, we extend the SSRSC by adopting the ranking weighted ensemble strategy, termed as weSSCOR. Extensive experiments have been conducted on two benchmark SSVEP datasets, and the results demonstrated that the proposed SSRSC/weSSRSC can significantly improve accuracy and ITR of SSVEP detection with less calibration data, which has great potential in designing high ITR SSVEP-BCIs with less calibration efforts.