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  • On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data

    Creator:
    Roth, Mark
    Abstract:
    This paper discusses opportunities for developments in spatial clustering methods to help leverage broad scale community science data for building species distribution models (SDMs). SDMs are critical tools that inform the science and policy needed to mitigate the impacts of climate change on biodiversity. Community science data span spatial and...
    Resource Type:
    Capstone Project
    Full Text:
    capstone_template_2021_filled On the Role of Spatial

  • On surrogate supervision multi-view learning

    Creator:
    Jin, Gaole
    Abstract:
    Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although information from the target view is sufficient for learning task. However, this work addresses a semi-supervised case of multi-view learning, the surrogate supervision...
    Resource Type:
    Masters Thesis
    Full Text:
    the UCI machine learning repository. In each of the datasets, we first normalize all the samples and

  • Multi-criteria average reward reinforcement learning

    Creator:
    Natarajan, Sriraam
    Abstract:
    Reinforcement learning (RL) is the study of systems that learn from interaction with their environment. The current framework of Reinforcement Learning is based on receiving scalar rewards, which the agent aims to maximize. But in many real world situations, tradeoffs must be made among multiple objectives. This necessitates the use...
    Resource Type:
    Masters Thesis
    Full Text:
    different objectives. In this thesis, we consider the problem of learning in the presence of time- varying

  • Calibrating recurrent sliding window classifiers for sequential supervised learning

    Creator:
    Joshi, Saket Subhash
    Abstract:
    Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part-of-speech tagging and text-to-speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (RSW) classifier, which maps some window of the input sequence plus some number...
    Resource Type:
    Masters Thesis
    Full Text:
    WEKA, the widely used Java based Machine Learning library de- veloped at the University of Waikato, NZ

  • Shrunken learning rates do not improve AdaBoost on benchmark datasets

    Creator:
    Forrest, Daniel L. K.
    Abstract:
    Recent work has shown that AdaBoost can be viewed as an algorithm that maximizes the margin on the training data via functional gradient descent. Under this interpretation, the weight computed by AdaBoost, for each hypothesis generated, can be viewed as a step size parameter in a gradient descent search. Friedman...
    Resource Type:
    Masters Thesis
    Full Text:
    . 20 BIBLIOGRAPHY [1] Blake, C. L. and Merz, C. J. UCI Repository of machine learning databases

  • Active learning of constraints for semi-supervised clustering

    Creator:
    Xiong, Sicheng
    Abstract:
    Semi-supervised clustering aims to improve clustering performance by considering user supervision in the form of pairwise constraints. In this paper, we study the active learning problem of selecting pairwise must-link and cannot-link constraints for semisupervised clustering. We consider active learning in an iterative manner where in each iteration queries are...
    Resource Type:
    Masters Thesis
    Full Text:
    , 21], the research on active learning of constraints for semi-supervised clustering is relatively

  • Learning greedy policies for the easy-first framework

    Creator:
    Xie, Jun
    Abstract:
    Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
    Resource Type:
    Masters Thesis
    Full Text:
    available and machine learning methods became more mature, there has been a lot of research aimed at resolv

  • Spatial supervised learning using recurrent sliding window classifiers

    Creator:
    Vega, Dan P.
    Abstract:
    Spatial Supervised Learning seeks to learn how to assign a label to each pixel in a spatial grid such as the pixels of remote-sensed images. The standard approach is to treat each grid cell separately and to use only the measured features of the grid cell to determine the assigned...
    Resource Type:
    Capstone Project and Presentation

  • Leveraging Compressive Sampling and Machine Learning for Adaptive and Cooperative Wideband Spectrum Sensing

    Creator:
    Zaid, Adem
    Abstract:
    This thesis proposes a novel technique that exploits spectrum occupancy behaviors inherent to wideband spectrum access to enable efficient cooperative spectrum sensing. The proposed technique reduces the number of required sensing measurements while accurately recovering spectrum occupancy information. It does so by leveraging compressive sampling theory to exploit the block-like...
    Resource Type:
    Masters Thesis
    Full Text:
    of Machine Learning Research, 12(Oct):2825–2830, 2011. [37] Z. Qin, Y. Gao, M. D. Plumbley, and C. G

  • Hierarchical average reward reinforcement learning

    Creator:
    Seri, Sandeep
    Abstract:
    Reinforcement Learning (RL) is the study of agents that learn optimal behavior by interacting with and receiving rewards and punishments from an unknown environment. RL agents typically do this by learning value functions that assign a value to each state (situation) or to each state-action pair. Recently, there has been...
    Resource Type:
    Masters Thesis
    Full Text:
    Reinforcement Learning Abstract approved: Prasad Tadepalli Reinforcement Learning (RL) is the study of agents