Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 필터 해제
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these meth…
Inductive LearningRepresentation LearningDeep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile …
Inductive LearningLink PredictionNetwork EmbeddingNode ClassificationSemi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such meth…
BIG-bench Machine LearningInductive LearningTransductive LearningInductive supervised quantum learning
In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arise…
Inductive LearningLearning TheoryA system of serial computation for classified rules prediction in non-regular ontology trees
Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multia…
Decision MakingInductive LearningMinimax Lower Bounds for Realizable Transductive Classification
Transductive learning considers a training set of $m$ labeled samples and a test set of $u$ unlabeled samples, with the goal of best labeling that particular test set. Conversely, inductive learning considers a training …
Binary ClassificationClassificationGeneral ClassificationInductive Learning+1A Theory of Formal Synthesis via Inductive Learning
Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refe…
BIG-bench Machine LearningInductive LearningLearning TheoryInductive Learning for Rule Generation from Ontology
This paper presents an idea of inductive learning use for rule generation from ontologies. The main purpose of the paper is to evaluate the possibility of inductive learning use in rule generation from ontologies and to …
Inductive LearningIncremental Learning of Event Definitions with Inductive Logic Programming
Event recognition systems rely on properly engineered knowledge bases of event definitions to infer occurrences of events in time. The manual development of such knowledge is a tedious and error-prone task, thus event-ba…
Activity RecognitionIncremental LearningInductive LearningInductive logic programmingPost-Proceedings of the First International Workshop on Learning and Nonmonotonic Reasoning
Knowledge Representation and Reasoning and Machine Learning are two important fields in AI. Nonmonotonic logic programming (NMLP) and Answer Set Programming (ASP) provide formal languages for representing and reasoning w…
BIG-bench Machine LearningInductive LearningInductive logic programmingNegation+1Theory-Based Inductive Learning: An Integration of Symbolic and Quantitative Methods
The objective of this paper is to propose a method that will generate a causal explanation of observed events in an uncertain world and then make decisions based on that explanation. Feedback can cause the explanation an…
Inductive LearningRegularized Boost for Semi-Supervised Learning
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various…
Ensemble LearningInductive LearningSolving Multiclass Learning Problems via Error-Correcting Output Codes
Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k ``classes''). The definition is acquired by studying collections of …
Inductive Learning