Category learningRule-basedSimilarity-basedEEG-fNIRS fusionCategorization is a fundamental ability in human cognition that enables generalization and promotes decision-making. A categorization problem can be solved by employing a rule-based or a similarity-based strategy. The current study aims to ...
网络学习 网络释义 1. 学习 ...代理㆟会整合系统内部的知识与收集的知识来执行归纳学习(similarity-based learning) 与演译学习(explanation-based lea… www.docin.com|基于 1 个网页
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One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective NeurIPS 2021 极智嘉联合马来西亚大学、英国Surrey大学提出全新数据哈希检索算法 - 物流指闻 一个可以应用于所有的损失:深度哈希与单一余弦相似度的基础上学习目标,马来西亚大学 摘要: 深度哈希模型通常有两个主要的学习目标:使...
Specifically, we propose a framework for defining the goodness of a (dis)similarity function with respect to a given learning task and propose algorithms that have guaranteed generalization properties when working with such good functions. Our framework unifies and generalizes the frameworks proposed by...
We also compare the performance of CB-SBIT against the performance of the open source transfer learning algorithm TransferBoost using text data. Our results show that CB-SBIT outperforms the original SBIT and SMOTE using varying sizes of network traffic data but falls short when compared to ...
CONNECTIVITY SIMILARITY BASED GRAPH LEARNING FOR INTERACTIVE MULTI-LABEL IMAGE SEGMENTATIONA system and method of connectivity-based image processing to identify and extract objects in image data. Variations on the method may include iterative local smoothing operations and various algorithmic solutions to ...
The SIMLR software identifies similarities between cells across a range of single-cell RNA-seq data, enabling effective dimension reduction, clustering and visualization. We present single-cell interpretation via multikernel learning (SIMLR), an analytic
Machine‐Learning‐Based Image Similarity Analysis for Use in Materials Characterizationmachine learningmaterials characterizationmicrostructural imagessimilarity analysistype="main" xml:lang="en">\n
Positive and unlabelled learning (PU learning) has been investigated to deal with the situation where only the positive examples and the unlabelled examples are available. Most of the previous works focus on identifying some negative examples from the unlabelled data, so that the supervised learning ...