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Titlebook: Knowledge Management and Acquisition for Intelligent Systems; 16th Pacific Rim Kno Kouzou Ohara,Quan Bai Conference proceedings 2019 Spring

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樓主: lexicographer
41#
發(fā)表于 2025-3-28 18:20:18 | 只看該作者
42#
發(fā)表于 2025-3-28 20:38:09 | 只看該作者
Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection,thers. However, this is quite challenging for computers as it is a subjective task which may be influenced by human emotional factors. Instead of focusing on how the models make reactions on datasets, our method has a capability of assigning scores to samples respectively within a dataset that estim
43#
發(fā)表于 2025-3-28 22:55:14 | 只看該作者
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發(fā)表于 2025-3-29 04:27:53 | 只看該作者
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發(fā)表于 2025-3-29 08:21:00 | 只看該作者
Finding Diachronic Objects of Drifting Descriptions by Similar Mentions, as the same over time and diversity descriptions that record actions on different objects. This research finds diachronic objects to extract a document subset of drift descriptions. We assumed that a diachronic object would be mentioned similarly and have different time-distribution appearances. Co
46#
發(fā)表于 2025-3-29 15:15:21 | 只看該作者
A Max-Min Conflict Algorithm for the Stable Marriage Problem,ge problem. We solve the problem in terms of a constraint satisfaction problem, i.e. find a complete assignment for men in which every man is assigned to a woman so that the assignment does not contain any blocking pairs. To do this, we apply a local search method in which a max-conflict heuristic i
47#
發(fā)表于 2025-3-29 15:43:46 | 只看該作者
48#
發(fā)表于 2025-3-29 23:34:32 | 只看該作者
Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Nare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faste
49#
發(fā)表于 2025-3-30 00:21:20 | 只看該作者
50#
發(fā)表于 2025-3-30 04:02:28 | 只看該作者
,Adaptive Database’s Performance Tuning Based on Reinforcement Learning,. With the hundreds of parameters to be considered under the diverse application configurations, business logic and software technology, getting a true global optimum setting is difficult for a DB administrator. We propose a novel approach based on Reinforcement Learning to tune a DB adaptively with
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