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Filtering by: Alternative title ssim Report no. 1654 Remove constraint Alternative title ssim: Report no. 1654 Advisor Dietterich, Thomas G. Remove constraint Advisor: Dietterich, Thomas G.

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  • 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:
    descent algorithm. For this report, we tested whether reduced learning rates can also improve

  • Learning functional descriptions from examples

    Creator:
    Flann, Nicholas S.
    Abstract:
    The task of inductive learning from examples places constraints on the representation of training instances and concepts. These constraints are different from, and often incompatible with, the constraints placed on the representation by the performance task. This incompatibility is severe when learning functional concepts and explains why previous researchers have...
    Resource Type:
    Masters Thesis
    Full Text:
    system requires virtually no "vocabulary engineering" to learn concepts in a new domain. Learning

  • A specialized ATMS for equivalence relations

    Creator:
    Koff, Caroline Nan
    Abstract:
    A specialized ATMS for efficiently computing equivalence relations in multiple contexts is introduced. This specialized ATMS overcomes the problems with existing solutions to reasoning with equivalence relations. The most direct implementation of an equivalence relation in the ATMS-encoding the reflexive, transitive, and symmetric rules in the consumer architecture-produces redundant equality...
    Resource Type:
    Masters Thesis
    Full Text:
    with the number of distinct assumptions. The special- ized ATMS presented here produces no redundant

  • Protein secondary structure prediction using conditional random fields and profiles

    Creator:
    Shen, Rongkun
    Abstract:
    Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
    Resource Type:
    Masters Thesis
    Full Text:
    , respectively. In addition, there was no apparent overfitting problem observed in our model. Besides

  • An empirical study into learning through experimentation

    Creator:
    Ruff, Ritchey Alvin
    Abstract:
    A key aspect of how we understand the world revolves around an ability to manipulate our surroundings to experiment. From the scientific method through theories of child development, the ability to experiment is deemed critical; however, few studies have been performed to understand the strengths and weaknesses of different experimental...
    Resource Type:
    Dissertation
    Full Text:
    the experiment selector had little or no effect. Our best situations were where: (a) only a small

  • Evaluation functions in aerial image segmentation

    Creator:
    Langford, William T.
    Abstract:
    Remote sensing is the most practical way to acquire large amounts of land cover data for monitoring and understanding environmental change, so it is important to be able to map land cover from imagery. Maps defining land cover patches as polygons rather than pixels greatly improve processing efficiency in models...
    Resource Type:
    Dissertation
    Full Text:
    generated from images is generally specified in terms of classification accuracy with no specification of

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation
    Full Text:
    message, no corrections have been made, then perhaps it is safe for the learning system to treat the

  • Grasshopper Infestation Prediction : An Application of Data Mining to Ecological Modeling

    Creator:
    Bunjongsat, Waranun
    Abstract:
    This thesis presents a case study of applying machine learning tools to build a predictive model of annual infestations of grasshoppers in Eastern Oregon. The purpose of the study was two-fold. First, we wanted to develop a predictive model. Second, we wanted to explore the capabilities of existing machine learning...
    Resource Type:
    Capstone Project
    Full Text:
    Values_______________________________________________________12 2.2.3 No Value___________________________________________________________________12 2.2.4 Noise

  • Classification context in a machine learning approach to predicting protein secondary structure

    Creator:
    Langford, Bill T.
    Abstract:
    An important problem in molecular biology is to predict the secondary structure of proteins from their primary structure. The primary structure of a protein is the sequence of amino acid residues. The secondary structure is an abstract description of the shape of the folded protein, with regions identified as alpha...
    Resource Type:
    Masters Thesis
    Full Text:
    58 5.3.5 Types of Simulated Data 60 Chapter 6 Baseline Experiments 61 6.1 No Classification

  • An implementation and initial test of generalized radial basis functions

    Creator:
    Wettschereck, Dietrich
    Abstract:
    Generalized Radial Basis Functions were used to construct networks that learn input-output mappings from given data. They are developed out of a theoretical framework for approximation based on regularization techniques and represent a class of three-layer networks similar to backpropagation networks with one hidden layer. A network using Gaussian base...
    Resource Type:
    Masters Thesis
    Full Text:
    , T.M., "The need for biases in learning generalizations", Report No. CBM-TR-117, Rutgers University