the proceedings contain 24 papers. the special focus in this conference is on Inductive Inference, learning and Information Extraction. the topics include: Abduction and the dualization problem;signal extraction and k...
ISBN:
(纸本)3540202919
the proceedings contain 24 papers. the special focus in this conference is on Inductive Inference, learning and Information Extraction. the topics include: Abduction and the dualization problem;signal extraction and knowledge discovery based on statistical modeling;association computation for information access;efficient data representations that preserve information;can learning in the limit be done efficiently;intrinsic complexity of uniform learning;on ordinal VC-dimension and some notions of complexity;learning of erasing primitive formal systems from positive examples;changing the inference type;robust inference of relevant attributes;efficient learning of ordered and unordered tree patterns with contractible variables;on the learnability of erasing pattern languages in the query model;learning of finite unions of tree patterns with repeated internal structured variables from queries;kernel trick embedded gaussian mixture model;efficiently learningthe metric with side-information;learning continuous latent variable models with bregman divergences;a stochastic gradient descent algorithm for structural risk minimisation;on the complexity of training a single perceptron with programmable synaptic delays;learning a subclass of regular patterns in polynomial time;identification with probability one of stochastic deterministic linear languages;criterion of calibration for transductive confidence machine with limited feedback;well-calibrated predictions from online compression models;transductive confidence machine is universal and on the existence and convergence of computable universal priors.
the proceedings contain 29 papers. the topics discussed include: meta GPT-based agent for enhanced phishing email detection;detecting CDN domain names based on resolution graph with PU learning;a new lattice-based str...
ISBN:
(纸本)9798400717994
the proceedings contain 29 papers. the topics discussed include: meta GPT-based agent for enhanced phishing email detection;detecting CDN domain names based on resolution graph with PU learning;a new lattice-based strong designated verifier signature scheme in the standard model;parallel compensation method for nonlinear impairments of optical fiber based on regular perturbation theory;an efficient and lightweight authenticated key agreement scheme cloud-assisted for smart agricultural monitoring systems;privacy protection in mobile crowdsensing based on local differential privacy;Argos: a detection-based method for log fusion and provenance graph compression;and a lightweight and trustworthy authentication protocol for UAV networks based on PUF.
Assessment remains a cornerstone of the educational process, with standardized testing often serving as a primary method for evaluating learning. However, as pedagogical approaches continue to evolve, Outcome-Based As...
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Additive preference representation is standard in Multiple Criteria Decision Analysis, and learning such a preference model dates back from the UTA method [11]. In this seminal work, an additive piece-wise linear mode...
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ISBN:
(纸本)9783031739026;9783031739033
Additive preference representation is standard in Multiple Criteria Decision Analysis, and learning such a preference model dates back from the UTA method [11]. In this seminal work, an additive piece-wise linear model is inferred from a learning set composed of pairwise comparisons. In this setting, the learning set is provided by a single Decision-Maker (DM), and an additive model is inferred to match the learning set. We extend this framework to the case where (i) multiple DMs with heterogeneous preferences provide part of the learning set, and (ii) the learning set is provided as a whole without knowing which DM expressed each pairwise comparison. Hence, the problem amounts to inferring a preference model for each DM and simultaneously "discovering" the segmentation of the learning set. In this paper, we show that this problem is computationally difficult. We propose a mathematical programming based resolution approach to solve this Preference learning and Segmentation problem (PLS). We also propose a heuristic to deal with large datasets. We study the performance of both algorithms through experiments using synthetic and real data.
this systematic literature review examines predictive analytics and machine learning (ML) applications in edu-cational settings from 2019 to 2023. Machine learning (ML) algorithms are increasingly used to predict stud...
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作者：
Lee, Kun Chang
Sungkyunkwan University Seoul03063 Korea Republic of
Material and human actors coexist in sociomaterial society, an ontologically philosophical sphere, in our new materialistic universe. Philosophically, sociomaterial civilizations think humans and materials are equal. ...
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作者：
Yeh, Chih-ChuanDepartment of Finance
National Taichung University of Science and Technology No.129 Sec. 3 Sanmin Rd. North Dist Taichung City40401 Taiwan
this article examines the transformative impact of Robo-Advisors on wealth management through the integration of big data and artificial intelligence (AI) in algorithmic trading strategies. We explore how AI technolog...
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this paper introduces a novel Large Language Model (LLM)-based system designed to enhance learning effect through Socratic inquiry, thereby fostering deep understanding and longterm knowledge consolidation. Recognizin...
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the theory of multiple intelligences is a subject that is widely valued by countries around the world today, and it is also an important part of education and teaching reform in various countries around the world. On ...
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