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Full-Text Articles in Physical Sciences and Mathematics

A Survey Of Transfer Learning Methods For Reinforcement Learning, Nicholas Bone Dec 2008

A Survey Of Transfer Learning Methods For Reinforcement Learning, Nicholas Bone

Computer Science Graduate and Undergraduate Student Scholarship

Transfer Learning (TL) is the branch of Machine Learning concerned with improving performance on a target task by leveraging knowledge from a related (and usually already learned) source task. TL is potentially applicable to any learning task, but in this survey we consider TL in a Reinforcement Learning (RL) context. TL is inspired by psychology; humans constantly apply previous knowledge to new tasks, but such transfer has traditionally been very difficult for—or ignored by—machine learning applications. The goals of TL are to facilitate faster and better learning of new tasks by applying past experience where appropriate, and to ...


Real-Time Automatic Price Prediction For Ebay Online Trading, Ilya Igorevitch Raykhel Nov 2008

Real-Time Automatic Price Prediction For Ebay Online Trading, Ilya Igorevitch Raykhel

Theses and Dissertations

While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of ...


Development Of A Workflow For The Comparison Of Classification Techniques, Zanifa Omary Sep 2008

Development Of A Workflow For The Comparison Of Classification Techniques, Zanifa Omary

Masters

As the interest in machine learning and data mining springs up, the problem of how to assess learning algorithms and compare classifiers become more pressing. This has been associated with the lack of comprehensive and complete workflow depending on the project scale to provide guidance to its users. This means the success or failure of the project can be highly dependent on the person or team carrying it. The standard practice adopted by many researchers and experimenters has been to follow steps or phases from existing workflows such as CRISP-DM, KDD and SASSEMMA. However, as machine learning and data mining ...


Assessing The Costs Of Sampling Methods In Active Learning For Annotation, James Carroll, Robbie Haertel, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi Jun 2008

Assessing The Costs Of Sampling Methods In Active Learning For Annotation, James Carroll, Robbie Haertel, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi

Faculty Publications

Traditional Active Learning (AL) techniques assume that the annotation of each datum costs the same. This is not the case when annotating sequences; some sequences will take longer than others. We show that the AL technique which performs best depends on how cost is measured. Applying an hourly cost model based on the results of an annotation user study, we approximate the amount of time necessary to annotate a given sentence. This model allows us to evaluate the effectiveness of AL sampling methods in terms of time spent in annotation. We acheive a 77% reduction in hours from a random ...


Comparison Of Machine Learning Algorithms For Modeling Species Distributions: Application To Stream Invertebrates From Western Usa Reference Sites, Margi Dubal May 2008

Comparison Of Machine Learning Algorithms For Modeling Species Distributions: Application To Stream Invertebrates From Western Usa Reference Sites, Margi Dubal

All Graduate Plan B and other Reports

Machine learning algorithms are increasingly being used by ecologists to model and predict the distributions of individual species and entire assemblages of sites. Accurate prediction of distribution of species is an important factor in any modeling. We compared prediction accuracy of four machine learning algorithms-random forests, classification trees, support vector machines, and gradient boosting machines to a traditional method, linear discriminant models (LDM), on a large set of stream invertebrate data collected at 728 reference sites in the western United States. Classifications were constructed for individual species and for assemblages of sites clustered a priori by similarity on biological characteristics ...


Machine Learning And Graph Theory Approaches For Classification And Prediction Of Protein Structure, Gulsah Altun Apr 2008

Machine Learning And Graph Theory Approaches For Classification And Prediction Of Protein Structure, Gulsah Altun

Computer Science Dissertations

Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this ...


Improving Liquid State Machines Through Iterative Refinement Of The Reservoir, R David Norton Mar 2008

Improving Liquid State Machines Through Iterative Refinement Of The Reservoir, R David Norton

Theses and Dissertations

Liquid State Machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, a reservoir, or liquid, is randomly created which acts as a filter for a readout function. We develop three methods for iteratively refining a randomly generated liquid to create a more effective one. First, we apply Hebbian learning to LSMs by building the liquid with spike-time dependant plasticity (STDP) synapses. Second, we create an eligibility based reinforcement learning algorithm for synaptic development. Third, we apply principles of Hebbian learning and reinforcement learning to create a new algorithm called separation driven synaptic modification ...


Machine Learning Approaches For Epitope Prediction, Yasser Mohamed El-Manzalawy Jan 2008

Machine Learning Approaches For Epitope Prediction, Yasser Mohamed El-Manzalawy

Graduate Theses and Dissertations

The identification and characterization of epitopes in antigenic sequences is critical for understanding disease pathogenesis, for identifying potential autoantigens, and for designing vaccines and immune-based cancer therapies. As the number of pathogen genomes fully or partially sequenced is rapidly increasing, experimental methods for epitope mapping would be prohibitive in terms of time and expenses. Therefore, computational methods for reliably identifying potential vaccine candidates (i.e., epitopes that invoke strong response from both T-cells and B-cells) are highly desirable.

Machine learning offers one of the most cost-effective and widely used approaches to developing epitope prediction tools. In the last few years ...


Learning Policies For Embodied Virtual Agents Through Demonstration, Jonathan Dinerstein, Parris K. Egbert, Dan A. Ventura Jan 2008

Learning Policies For Embodied Virtual Agents Through Demonstration, Jonathan Dinerstein, Parris K. Egbert, Dan A. Ventura

Faculty Publications

Although many powerful AI and machine learning techniques exist, it remains difficult to quickly create AI for embodied virtual agents that produces visually lifelike behavior. This is important for applications (e.g., games, simulators, interactive displays) where an agent must behave in a manner that appears human-like. We present a novel technique for learning reactive policies that mimic demonstrated human behavior. The user demonstrates the desired behavior by dictating the agent’s actions during an interactive animation. Later, when the agent is to behave autonomously, the recorded data is generalized to form a continuous state-to-action mapping. Combined with an appropriate ...