To this end, a novel weakly supervised, multi-instance learning algorithm is designed to learn instance-wise vehicle detectors from such "weak labels".
It is shown that, in a restrictive case (but consistent to prevent overfitting), the problem boils down to a multiple kernel learning instance with infinitely many kernels.
Each sample represented one machine learning instance with gene expression values as features and 'pluripotent' or 'non-pluripotent' as class labels.
We repeat this 100 times, randomizing the instances in the learning and testing groups, and the order of the input learning instances.
We learn, for instance, that Annie smokes "intensely, staring at her naked feet".
We learn, for instance, that Mitchell Crook does not use fabric softener on his sheets.
We learn, for instance, that "almost to a man, Muslim clerics in their sermons" endorsed the 9/11 attacks.
You will learn, for instance, that the leopard gecko uses its long tongue to clean itself, including its eyeballs.
We learn, for instance, that Brown first became a killer to avenge the murder of his grandparents.
Instead, fittingly playful author biographies are at the back, where we learn, for instance, that Beryl Bainbridge's home contained a stuffed water buffalo.
You'll learn, for instance, that while Fidel Castro was a pitcher, the oft-repeated claim that he was a major league prospect is apocryphal.
