this might be considered a hack but - in a neural net classifier (such as a multi-layer perceptron trained through backpropagation) the weight of an individual example could be manipulated by including many copies of that example in the training set
the neural net package is the goto in R and there are many nice tutorials out there which would work for the purpose of testing the effect of varying the proportion of the repeated example to give it extra weighting
As a little wilder of a suggestion (although not in r) - in cognitive science there is also a concept known as selective attention that can be applied to a classification/category learning problem. check out ALCOVE (kruschke, 1992) or SUSTAIN (love et al., 2004) - a more modern approach if these seem interesting
Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological review, 99(1), 22.
implemented in matlab - https://github.com/noconaway/ALCOVE
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological review, 111(2), 309.