1. A learning apparatus for learning data to be used by a detection apparatus, the detection apparatus being adapted for judging whether provided data is a detection target or not by using a data set including plural learning samples each of which has data weighting set thereon and has been labeled as a detection target or non-detection target, the learning apparatus comprising: one or more programmable central processing units, and a computer-readable storage medium encoded with instructions executable by at least one of the central processing units and operatively coupled to at least one or the central processing units, the instructions comprising instructions that comprise a weak hypothesis generation unit for generating a weak hypothesis for estimating whether provided data is a detection target or not, instructions that comprise a reliability calculation unit for calculating reliability of the weak hypothesis on the basis of the result of estimation of the weak hypothesis generated by the weak hypothesis generation unit with respect to the data set, and instructions that comprise a data weighting update unit for updating the data weighting in such a manner that the data weighting of a learning sample on which estimation by the weak hypothesis generated by the weak hypothesis generation unit is incorrect becomes relatively larger than the data weighting of a learning sample on which estimation is correct; wherein the weak hypothesis generation unit comprises a selection unit for selecting a part of plural weak hypotheses and selecting one or plural weak hypotheses having higher estimation performance with respect to the data set from the selected part of the weak hypotheses, as high-performance weak hypotheses, a threshold value learning unit for calculating and adding the product of the result of estimation of the weak hypothesis with respect to the data set and the reliability of the weak hypothesis every time a weak hypothesis is selected by the weak hypothesis selection unit and learning an abort threshold value for aborting the processing by the detection apparatus to judge whether the provided data is a detection target or not on the basis of the result of the addition, a new weak hypothesis generation unit for generating one or more new weak hypotheses by adding a predetermined modification to the high-performance weak hypotheses, and a weak hypothesis selection unit for selecting one weak hypothesis having the highest estimation performance with respect to the data set from the high-performance weak hypotheses and the new weak hypotheses, the weak hypothesis generation unit repeating processing to generate a weak hypothesis every time the data weighting is updated by the data weighting update unit; and wherein the threshold value learning unit learns the abort threshold value on the basis of the result of addition of the product of the result of estimation of the weak hypothesis with respect to positive data labeled as the detection target, of the data set, and the reliability of the weak hypothesis, calculated and added every time a weak hypothesis is selected by the weak hypothesis selection unit.