203 research outputs found

    Learning Small Trees and Graphs that Generalize

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    In this Thesis we study issues related to learning small tree and graph formed classifiers. First, we study reduced error pruning of decision trees and branching programs. We analyze the behavior of a reduced error pruning algorithm for decision trees under various probabilistic assumptions on the pruning data. As a result we get, e.g., new upper bounds for the probability of replacing a tree that fits random noise by a leaf. In the case of branching programs we show that the existence of an efficient approximation algorithm for reduced error pruning would imply P=NP. This indicates that reduced error pruning of branching programs is most likely impossible in practice, even though the corresponding problem for decision trees is easily solvable in linear time. The latter part of the Thesis is concerned with generalization error analysis, more particularly on Rademacher penalization applied to small or otherwise restricted decision trees. We develop a progressive sampling method based on Rademacher penalization that yields reasonable data dependent sample complexity estimate

    2013 Wild Blueberry Project Reports

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    The 2013 edition of the Wild Blueberry Project Reports was prepared for the Wild Blueberry Commission of Maine and the Wild Blueberry Advisory Committee by researchers at the University of Maine, Orono. Projects in this report include: 1. Development of effective intervention measures to maintain and improve food safety for wild blueberries 2. Do wild blueberries alleviate risk factors related to the Metabolic Syndrome? 3. Wild Blueberry consumption and exercise-induced Oxidative Stress: Inflammatory Response and DNA damage 4. Control tactics for blueberry pest insects, 2013 5. Pesticide residues on wild blueberry, 2013 6. Biology of pest insects and IPM, 2013 7. Biology of blueberry, beneficial insects, and blueberry pollination 8. Biology of spotted wing drosophila, 2013 9. Maine wild blueberry –mummy berry research and extension 10. Evaluation of fungicides for control of mummy berry on lowbush blueberry (2013) 11. Wild blueberry Extension Education Program in 2013 INPUT SYSTEMS STUDY: 12. Systems approach to improving the sustainability of wild blueberry production, Year Four of a four-year study – experimental design 13. Food safety- Prevalence study of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella spp. on lowbush blueberries (Vaccinium angustifolium) 14. Agronomic input effects on sensory quality and chemical composition of wild Maine blueberries 15. Systems approach to improving the sustainability of wild blueberry production, Year four of a four-year study – reports from Frank Drummond 16. Systems approach to improving the sustainability of wild blueberry production, Year 4 of a four-year study, disease management results 17. Systems approach to improving the sustainability of wild blueberry production, Year Four of a four-year study, weed management results 18. Phosphorus and organic matter interactions on short-range ordered minerals in acidic barren soils 19. Systems approach to improving the sustainability of wild blueberry production, preliminary economic comparison for 2012-13 20. Ancillary projects in disease research (ancillary study) 21. Systems approach to improving the sustainability of wild blueberry production – Ancillary land-leveling study, Year Three of a four-year study (ancillary study) 22. Pre-emergent combinations of herbicides for weed control in wild blueberry fields – 2013 results from the 2012 trial (ancillary study) 23. Evaluation of herbicides for 2012 prune year control of fineleaf sheep fescue in wild blueberries – 2013 crop year results (ancillary study) 24. 2012 pre-emergence application timing and rate of Alion and Sandea in combination with Velpar or Sinbar – 2013 yields (ancillary study) 25. Pre-emergence Sinbar combinations for weed control in a non-crop wild blueberry field – 2012-2014 (ancillary study) 26. Evaluation of three pre-emergence herbicides alone and in combination with Velpar or Sinbar for effects on wild blueberry productivity and weed control (ancillary study) 27. Post-harvest control of red sorrel in a non-crop blueberry field, 2012-2014 (ancillary study) 28. Compost and mulch effects on soil health and nutrient dynamics in wild blueberry (ancillary study) 29. Evaluation of conventional and organic fertilizers on blueberry growth and yield (ancillary study

    Planning under time pressure

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    Heuristic search is a technique used pervasively in artificial intelligence and automated planning. Often an agent is given a task that it would like to solve as quickly as possible. It must allocate its time between planning the actions to achieve the task and actually executing them. We call this problem planning under time pressure. Most popular heuristic search algorithms are ill-suited for this setting, as they either search a lot to find short plans or search a little and find long plans. The thesis of this dissertation is: when under time pressure, an automated agent should explicitly attempt to minimize the sum of planning and execution times, not just one or just the other. This dissertation makes four contributions. First we present new algorithms that use modern multi-core CPUs to decrease planning time without increasing execution. Second, we introduce a new model for predicting the performance of iterative-deepening search. The model is as accurate as previous offline techniques when using less training data, but can also be used online to reduce the overhead of iterative-deepening search, resulting in faster planning. Third we show offline planning algorithms that directly attempt to minimize the sum of planning and execution times. And, fourth we consider algorithms that plan online in parallel with execution. Both offline and online algorithms account for a user-specified preference between search and execution, and can greatly outperform the standard utility-oblivious techniques. By addressing the problem of planning under time pressure, these contributions demonstrate that heuristic search is no longer restricted to optimizing solution cost, obviating the need to choose between slow search times and expensive solutions

    Selective search in games of different complexity

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    WOPR in search of better strategies for board games

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    Board games have been challenging intellects and capturing imaginations for more than five thousand years. Researchers have been producing artificially intelligent players for more than fifty. Common artificial intelligence techniques applied to board games use alpha-beta pruning tree search techniques. This paper supplies a frame- work that accommodates many of the diverse aspects of board games, as well as exploring several alternatives for searching out ever better strategies. Techniques examined include optimizing artificial neural networks using genetic algorithms and backpropagation

    Probabilistic representation and manipulation of Boolean functions using free Boolean diagrams

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 145-149).by Amelia Huimin Shen.Ph.D

    2012 Wild Blueberry Project Reports

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    The 2012 edition of the Wild Blueberry Project Reports was prepared for the Wild Blueberry Commission of Maine and the Wild Blueberry Advisory Committee by researchers at the University of Maine, Orono. Projects in this report include: 1. Do wild blueberries alleviate risk factors related to the Metabolic Syndrome? 2. Development of effective intervention measures to maintain and improve food safety for wild blueberries 3. Control tactics for blueberry pest insects, 2012 4. Development and implementation of a wild blueberry thrips IPM program, 2012 5. IPM 6. Biology of blueberry and pest insects, 2012 7. Biology of beneficial insects and blueberry pollination, 2012 8. Pesticide residues on lowbush blueberry, 2012 9. Maine wild blueberry –mummy berry research and extension 10. Efficacy of Apogee growth regulator for stimulating rhizome growth into bare spots in wild blueberry fields 11. Velpar by Matrix pre and post-emergence applications - demonstration plots 12. Wild blueberry Extension Education Program in 2012 INPUT SYSTEMS STUDY: 13. Systems approach to improving the sustainability of wild blueberry production, Year Three of a four-year study – experimental design 14. Food safety- Prevalence study of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella spp. on lowbush blueberries (Vaccinium angustifolium) 15. Abundance of insect pest species and natural enemies in lowbush blueberry fields maintained under different management practices 16. Input Systems Study: Systems approach to improving the sustainability of wild blueberry production, Year 3 of a four-year study, disease management results 17. Plant productivity, Year Three of a four-year study 18. Systems approach to improving the sustainability of wild blueberry production, Year Three of a four-year study, weed management results 19. Effects of organic and conventional management systems on the phosphorus solubility of lowbush blueberry barren soils 20. Systems approach to improving sustainability of wild blueberry production – soil health and chemistry measures 21. Evaluation of fungicides for control of mummy berry disease (ancillary study) 22. Systems approach to improving the sustainability of wild blueberry production – Ancillary land-leveling study, Year Two of a four-year study (ancillary study) 23. Pre-emergent combinations of herbicides for weed control in wild blueberry fields – 2012 results from the 2011 trial (ancillary study) 24. Pre-emergent combinations of herbicides for weed control in wild blueberry fields – 2012 trial (ancillary study) 25. Evaluation of herbicides for control of fineleaf sheep fescue for grass control in wild blueberries (ancillary study) 26. Pre-emergence application timing and rate of Alion and Sandea in combination with Velpar or Sinbar on weed control and injury to wild blueberry (ancillary study) 27. Compost and mulch effects on soil health and nutrient dynamics in wild blueberry (ancillary study

    Contingent planning under uncertainty via stochastic satisfiability

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    We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSAT) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSAT problems and the solution of stochastic planning problems. This is the first work extending the planning-as-satisfiability paradigm to stochastic domains. Our planner, ZANDER, can solve arbitrary, goal-oriented, finite-horizon partially observable Markov decision processes (POMDPs). An empirical study comparing ZANDER to seven other leading planners shows that its performance is competitive on a range of problems. © 2003 Elsevier Science B.V. All rights reserved

    User Interaction in Deductive Interactive Program Verification

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