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- Title
- ANYTIME ACTIVE LEARNING DISSERTATION
- Creator
- Ramirez Loaiza, Maria E.
- Date
- 2016, 2016-05
- Description
-
Machine learning is a subfield of artificial intelligence which deals with algorithms that can learn from data. These methods provide...
Show moreMachine learning is a subfield of artificial intelligence which deals with algorithms that can learn from data. These methods provide computers with the ability to learn from past data and make predictions for new data. A few examples of machine learning applications include automated document categorization, spam detection, speech recognition, face detection and recognition, language translation, and self-driving cars. A common scenario for machine learning is supervised learning where the algorithm analyzes known examples to train a model that can identify a concept. For instance, given example documents that are pre-annotated as personal, work, family, etc., a machine learning algorithm can be trained to automate organizing your documents folder. In order to train a model that makes as few mistakes as possible, the algorithm needs many training examples (e.g., documents and their categories). Obtaining these examples often involves consulting the human user/expert whose time is limited and valuable. Hence, the algorithm needs to utilize the human’s time as efficiently as possible by focusing on the most cost-effective and informative examples that would make learning more efficient. Active learning is a technique where the algorithm selects which examples would be most cost-effective and beneficial for consultation with the human. In a typical active learning setting, the algorithm simply chooses the examples that should be asked to the expert. In this thesis, we take this one step further: we observe that we can make even better use of the expert’s time by showing not the full example but only the relevant pieces of it, so that the expert can focus on what is relevant and can provide the answer faster. For example, in document classification, the expert does not need to see the full document to categorize it; if the algorithm can show only the relevant snippet to the expert, the expert should be able to categorize the document much faster. However, automatically finding the relevant snippet is not a trivial task; showing an incorrect snippet can either hinder the expert’s ability to provide an answer at all (if the snippet is irrelevant) or even cause the expert to provide incorrect information (if the snippet is misleading). For this to work, the algorithm needs to find a snippet to show the expert, estimate how much time the expert will spend on that snippet, and predict if the expert will return an answer at all. Further, the algorithm would estimate the likelihood of the expert returning the correct answer. Similar to anytime algorithms that can find better solutions as they are given more time, we call the proposed set of methods anytime active learning where the experts are expected to give better answers as they are shown longer snippets. In this thesis, we focus on three aspects of anytime active learning: i) anytime active learning with document truncation where the algorithm assumes that the first words, sentences, and paragraphs of the document are most informative and it has to decide on the snippet length, i.e., where to truncate the document, ii) given a document, the algorithm optimizes for both snippet location and length, and lastly, iii) the algorithm chooses not only the snippet location and size but also chooses which documents to choose snippets from so that the snippet length, the correctness of the expert’s response, and the informativeness of the document are all optimized in a unified framework.
Ph.D. in Computer Science, May 2016
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