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Full-Text Articles in Physical Sciences and Mathematics
Clustering Of Multiple Instance Data., Andrew D. Karem
Clustering Of Multiple Instance Data., Andrew D. Karem
Electronic Theses and Dissertations
An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is …
Studying And Handling Iterated Algorithmic Biases In Human And Machine Learning Interaction., Wenlong Sun
Studying And Handling Iterated Algorithmic Biases In Human And Machine Learning Interaction., Wenlong Sun
Electronic Theses and Dissertations
Algorithmic bias consists of biased predictions born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, the interaction between people and algorithms can exacerbate bias such that neither the human nor the algorithms receive unbiased data. Thus, algorithmic bias can be introduced not only before and after the machine learning process but sometimes also in the middle of the learning process. With a handful of exceptions, only a few categories of bias have been studied in Machine Learning, and there are few, if any, studies of the impact of bias on both human behavior and algorithm performance. …