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检索条件"任意字段=18th International Conference on Algorithmic Learning Theory"

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algorithmic learning theory 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Marcus Hutter Rocco A. Servedio Eiji Takimoto

ISBN: (数字)9783540752257

ISBN: (纸本)9783540752240

this volume contains the papers presented at the 18th international Conf- ence on algorithmic learning theory (ALT 2007), which was held in Sendai (Japan) during October 1–4, 2007. the main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference. the conference was co-located with the Tenth international conference on Discovery Science (DS 2007). this volume includes 25 technical contributions that were selected from 50 submissions by the ProgramCommittee. It also contains descriptions of the ?ve invited talks of ALT and DS; longer versions of the DS papers are available in the proceedings of DS 2007. these invited talks were presented to the audience of both conferences in joint sessions.

关键词： Artificial Intelligence Data Mining and Knowledge Discovery

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the Item Response theory Model for an AI-based Adaptive learning System 18

The Item Response Theory Model for an AI-based Adaptive Lear...

引用

18th international conference on Information Technology Based Higher Education and Training (IthET)

作者： Cui, Wei Xue, Zhen Shen, Jun Sun, Geng Li, Jianxin Squirrel AI Learning Inc Shanghai Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia Deakin Univ Sch Informat Technol Melbourne Vic Australia

ISBN: (纸本)9781728124643

Item characteristics (e.g. item difficulty), and students' latent traits (e.g. student ability) are essential in a personalized learning system. In such system, items of different characteristics need to be recommended according to students' latent traits. the item response theory can fulfill such requirements. In this paper, we present a detailed description of the student ability and the item difficulty measurement based on an item response theory model. the model is evaluated using real data regarding the math and the English curriculum gathered from students' learning processes during the usage of the Squirrel AI learning System. the data is regarding the math and the English curriculum. It is proved that the difficulty evaluated by teachers minus the difficulty estimated from the data approximately follows a normal distribution. the mean value of students' abilities approaches 0.50 as the numbers of their attempts on questions increase.

关键词： Item difficulty Ability measurement Item response theory Big data Logistic model Maximum likelihood estimation

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Malthusian Reinforcement learning 18

Malthusian Reinforcement Learning

引用

18th international conference on Autonomous Agents and MultiAgent Systems (AAMAS)

作者： Leibo, Joel Z. Perolat, Julien Hughes, Edward Wheelwright, Steven Marblestone, Adam H. Duenez-Guzman, Edgar Sunehag, Peter Dunning, Iain Graepel, thore DeepMind London England

ISBN: (纸本)9781450363099

Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation. In Malthusian RL, increases in a subpopulation's average return drive subsequent increases in its size, just as thomas Malthus argued in 1798 was the relationship between preindustrial income levels and population growth [24]. Malthusian reinforcement learning harnesses the competitive pressures arising from growing and shrinking population size to drive agents to explore regions of state and policy spaces that they could not otherwise reach. Furthermore, in environments where there are potential gains from specialization and division of labor, we show that Malthusian reinforcement learning is better positioned to take advantage of such synergies than algorithms based on self-play.

关键词： Intrinsic motivation Adaptive radiation Demography Evolution Artificial general intelligence

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GA-Based Attempts to Improve the Recognition Rate and Generalization Capacity of the Nonlinear Soft Margin Support Vector Machines 18

GA-Based Attempts to Improve the Recognition Rate and Genera...

引用

18th international conference on System theory, Control and Computing (ICSTCC)

作者： State, Luminita Cocianu, Catalina Mircea, Marinela Univ Pitesti Dept Math & Informat Pitesti Romania Bucharest Univ Econ Studies Dept Informat & Cybernet Bucharest Romania

ISBN: (纸本)9781479946013

the aim of the paper is to report a new method based on genetic computation of designing a nonlinear soft margin SVM yielding to significant improvements in discriminating between two classes. the design of the SVM is performed in a supervised way, in general the samples coming from the classes being nonlinearly separable. the experimental analysis was performed on artificially generated data as well as on Ripley and MONK's datasets reported in the fourth section of the paper. the tests proved real improvements of both the recognition rate and generalization capacities without significantly increasing the computational complexity.

关键词： machine learning non-linear support vector machines soft margin SVM genetic algorithm kernel functions classifier design and evaluation model-free learning data driven control of parameters

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Feasible iteration of feasible learning functionals

Feasible iteration of feasible learning functionals

引用

18th international conference on algorithmic learning theory

作者： Case, John Koetzing, Timo Paddock, Todd Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Majest Res New York NY 10020 USA

ISBN: (纸本)9783540752240

For learning functions in the limit, an algorithmic learner obtains successively more data about a function and calculates trials each resulting in the output of a corresponding program, where, hopefully, these programs eventually converge to a correct program for the function. the authors desired to provide a feasible version of this learning in the limit - a version where each trial was conducted feasibly and there was some feasible limit on the number of trials allowed. Employed were basic feasible functionals which query an input function as to its values and which provide each trial. An additional tally argument 0(i) was provided to the functionals for their execution of the i-th trial. In this way more time resource was available for each successive trial. the mechanism employed to feasibly limit the number of trials was to feasibly count them down from some feasible notation for a constructive ordinal. Since all processes were feasible, their termination was feasibly detectable, and, so, it was possible to wait for the trials to terminate and suppress all the output programs but the last. Hence, although there is still an iteration of trials, the learning was a special case of what has long been known as total Fin-learning, i.e., learning in the limit, where, on each function, the learner always outputs exactly one conjectured program. Our general main results provide for strict learning hierarchies where the trial count down involves all and only notations for infinite limit ordinals. For our hierarchies featuring finitely many limit ordinal jumps, we have upper and lower total run time bounds of our feasible Fin-learners in terms of finite stacks of exponentials. We provide, though, an example of how to regain feasibility by a suitable parameterized complexity analysis.

关键词： learning systems

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Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots 18

Prediction in Intelligence: An Empirical Comparison of Off-p...

引用

18th international conference on Autonomous Agents and MultiAgent Systems (AAMAS)

作者： Rafiee, Banafsheh Ghiassian, Sina White, Adam Sutton, Richard S. Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (纸本)9781450363099

the ability to continually make predictions about the world may be central to intelligence. Off-policy learning and general value functions (GVFs) are well-established algorithmic techniques for learning about many signals while interacting with the world. In the past couple of years, many ambitious works have used off-policy GVF learning to improve control performance in both simulation and robotic control tasks. Many of these works use semi-gradient temporal-difference (TD) learning algorithms, like Q-learning, which are potentially divergent. In the last decade, several TD learning algorithms have been proposed that are convergent and computationally efficient, but not much is known about how they perform in practice, especially on robots. In this work, we perform an empirical comparison of modern off-policy GVF learning algorithms on three different robot platforms, providing insights into their strengths and weaknesses. We also discuss the challenges of conducting fair comparative studies of off-policy learning on robots and develop a new evaluation methodology that is successful and applicable to a relatively complicated robot domain.

关键词： artificial intelligence robotics reinforcement learning off-policy learning temporal-difference learning general value functions

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A Systems theoretic Approach to Online Machine learning 18

A Systems Theoretic Approach to Online Machine Learning

引用

18th Annual IEEE international Systems conference (SysCon)

作者： du Preez, Anli Beling, Peter Cody, Tyler Virginia Tech Grado Dept Ind & Syst Engn Blacksburg VA 24061 USA Virginia Tech Responsible Gen Intelligence Lab Arlington VA 24061 USA

ISBN: (纸本)9798350358810;9798350358803

the machine learning formulation of online learning is incomplete from a systems theoretic perspective. Typically, machine learning research emphasizes domains and tasks, and a problem solving worldview. It focuses on algorithm parameters, features, and samples, and neglects the perspective offered by considering system structure and system behavior or dynamics. Online learning is an active field of research and has been widely explored in terms of statistical theory and computational algorithms, however, in general, the literature still lacks formal system theoretical frameworks for modeling online learning systems and resolving systems-related concept drift issues. Furthermore, while the machine learning formulation serves to classify methods and literature, the systems theoretic formulation presented herein serves to provide a framework for the top-down design of online learning systems, including a novel definition of online learning and the identification of key design parameters. the framework is formulated in terms of input-output systems and is further divided into system structure and system behavior. Concept drift is a critical challenge faced in online learning, and this work formally approaches it as part of the system behavior characteristics. Healthcare provider fraud detection using machine learning is used as a case study throughout the paper to ground the discussion in a real-world online learning challenge.

关键词： machine learning online learning systems theory learning theory

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Characteristic of reviewer's assessments of individual learning performance Analysis of instructors' assessments using IRT 18

Characteristic of reviewer's assessments of individual learn...

引用

18th international conference on Information Technology Based Higher Education and Training (IthET)

作者： Nakayama, Minoru Tokyo Inst Technol Informat & Commun Engn Meguro Ku Tokyo Japan

ISBN: (纸本)9781728124643

the relationships between rating scores of instructors and student peers were analysed using an open data set and an item response theory (IRT) model. the characteristics of report rating behaviours of instructors and peers were compared, and the preference by peers of giving moderate ratings was confirmed. Also, the deviations in instructor ratings were illustrated using an IRT technique and examined. Finally, it was determined that the means of the peer's assessments matched the means of the estimated instructor's assessments with an IRT almost exactly.

关键词： Assessment characteristics Peer assessment IRT

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HIERARCHICAL INVARIANT SPARSE MODELING FOR IMAGE ANALYSIS

HIERARCHICAL INVARIANT SPARSE MODELING FOR IMAGE ANALYSIS

引用

18th IEEE international conference on Image Processing (ICIP)

作者： Bar, Leah Sapiro, Guillermo Tel Aviv Univ Tel Aviv Israel Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781457713033

Sparse representation theory has been increasingly used in signal processing and machine learning. In this paper we introduce a hierarchical sparse modeling approach which integrates information from the image patch level to derive a mid-level invariant image and pattern representation. the proposed framework is based on a hierarchical architecture of dictionary learning for sparse coding in a cortical (log-polar) space, combined with a novel pooling operator which incorporates the Rapid transform and max pooling to attain rotation and scale invariance. the invariant sparse representation of patterns here presented-can be used in different object recognition tasks. Promising results are obtained for three applications - 2D shapes classification, texture recognition and object detection.

关键词： Feature extraction dictionary learning sparse coding hierarchical models invariant representation

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Poster: Unraveling Reward Functions for Head-to-Head Autonomous Racing in AWS DeepRacer 23

Poster: Unraveling Reward Functions for Head-to-Head Autonom...

引用

international Symposium on theory, algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc)

作者： Tian, Allen John, Eddy Guerra Yang, Kecheng Univ Chicago Chicago IL 60637 USA Univ Houston Downtown Houston TX USA Texas State Univ San Marcos TX USA

ISBN: (纸本)9781450399265

AWS DeepRacer is a fully autonomous 1/18th scale race car designed to help developers learn and practice reinforcement learning through cloud-based simulations and real-world racing. What drives the reinforcement learning model is the reward function, a way to provide positive or negative feedback to an agent, guiding its learning process in reinforcement learning by assigning numerical values. In the AWS training environment, there are multiple modes in which you can run training simulations and evaluations in. these modes include Time Trial, Object Avoidance, and a relatively new mode: Head to Bot. the research done in this project was primarily focused on testing reward functions in the Head to Bot mode as well as developing a research function that would be suited for training in this new Head to Bot mode. the developed algorithm outperformed the default Centerline reward function, as well as the Object Avoidance reward function.

关键词： machine learning artificial intelligence reinforcement learning reward function autonomous racing

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