Transductive Reasoning Research Articles

Hybrid Inductive Graph Method for Matrix Completion

The recommender system can be viewed as a matrix completion problem, which aims to predict unknown values within a matrix. Solutions to this problem are categorized into two approaches: transductive and inductive reasoning. In transductive reasoning, the model cannot be applied to new cases unseen during training. In contrast, IGMC, the state-of-the-art inductive algorithm, only requires subgraphs for target users and items, without needing any other content information. While the absence of a requirement for content information simplifies the model and enhances transferability to new tasks, incorporating content information could still improve the model's performance. In this article, the authors introduce Hi-GMC, a hybrid version of the IGMC model that incorporates content information alongside users and items. They present a novel graph model to encapsulate the side information related to users and items and develop a learning method based on graph neural networks. This proposed method achieves state-of-the-art performance on the MovieLens-100K dataset for both warm and cold start scenarios.

Learning to Learn Better for Video Object Segmentation

Recently, the joint learning framework (JOINT) integrates matching based transductive reasoning and online inductive learning to achieve accurate and robust semi-supervised video object segmentation (SVOS). However, using the mask embedding as the label to guide the generation of target features in the two branches may result in inadequate target representation and degrade the performance. Besides, how to reasonably fuse the target features in the two different branches rather than simply adding them together to avoid the adverse effect of one dominant branch has not been investigated. In this paper, we propose a novel framework that emphasizes Learning to Learn Better (LLB) target features for SVOS, termed LLB, where we design the discriminative label generation module (DLGM) and the adaptive fusion module to address these issues. Technically, the DLGM takes the background-filtered frame instead of the target mask as input and adopts a lightweight encoder to generate the target features, which serves as the label of the online few-shot learner and the value of the decoder in the transformer to guide the two branches to learn more discriminative target representation. The adaptive fusion module maintains a learnable gate for each branch, which reweighs the element-wise feature representation and allows an adaptive amount of target information in each branch flowing to the fused target feature, thus preventing one branch from being dominant and making the target feature more robust to distractor. Extensive experiments on public benchmarks show that our proposed LLB method achieves state-of-the-art performance.

Analysis of mathematical inductive reasoning ability reviewed from student learning styles in mathematics learning

The purpose of this study is to describe students' mathematical inductive reasoning ability viewed from visual, auditorial, and kinesthetic learning styles in the Sequence and Series material. The type of research used is descriptive research with a qualitative approach. The instruments of this study were mathematical inductive reasoning ability tests, learning style questionnaires, and interview guidelines. The subjects of this study were six students of Class XI MIPA-2 SMA Negeri 1 Kota Serang that representing each learning style. The results showed that 1) students with a visual learning style were able to do transductive reasoning, analogies, used relationship patterns to analyze situations, compiled conjectures and could estimate answers, and they were able to explain patterns; 2) students with auditorial learning styles were able to do transductive reasoning, analogies, used relationship patterns to analyze situations, compiled conjectures and could estimate answers; 3) students with a kinesthetic learning style were able to do transductive reasoning, analogies, could use relationship patterns to analyze situations and compiled conjectures, and they were able to make generalizations.

Participatory action planning as transductive reasoning: towards the right to the city in Los Arenales, Antofagasta, Chile

Abstract This article discusses the experience of creating an overall plan for developing an informal settlement in the Chilean city of Antofagasta. This was accomplished by applying transductive reasoning to a community urban planning practice that aims to advance the community towards achieving the right to the city. By doing so, the article elaborates on insights gathered from the application of transductive reasoning as a method developed by Henri Lefebvre to reflect on the possible futures of cities using a bottom-up approach, thus utilizing the right to the city as a political agenda. Thus, this intervention provides an empirical reflection on the scope of action research in which the ideas of Henri Lefebvre are suitable for transforming utopian thinking into a political agenda for social transformation by a grassroots organization. The conclusions express the critiques of other authors whose approaches to Henri Lefebvre’s methods are substantially theoretical, thus wasting its empirical potential and capacity to engage communities.

KEMAMPUAN PENALARAN MATEMATIK SISWA MTs

The reasoning is the ability to find a way to get the truth. In terminology, logic is the science that governs the process of human thinking so that the results presented can reach the truth. So it can be concluded also that by reasoning, humans can make an argument that is absolute and can be accepted by others through logical thinking. This research was conducted to determine the mathematical reasoning ability of MTs class 8 students on Triangle and Quadrilateral material based on indicators of mathematical reasoning ability that is to draw a logical conclusion and compose an argument, analogical reasoning, transductive reasoning, deductive reasoning, generalization and estimate answers, solutions or tendency with non-routine troubleshooting. The research method used is descriptive qualitative with data to be analyzed is qualitative data in the form of written and oral answers obtained from the written test. This research was conducted in MTs PPI 38 Padalarang with the subject of this research is class VIII-A which amounted to 31 people. Based on the results of research, students' mathematical reasoning abilities on MTs PPI 38 Padalarang are classified as sufficient. This is because there are some reasoning indicators that still need to be improved especially on analytical reasoning and generalization indicator as stated in the table which has been described.

Reliable reasoning: induction and statistical learning theory

In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors -- a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.

Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach

The paper is offering a comparative study of major modeling and pattern discovery approaches applicable to the area of data analysis and decision support systems in general, and to the area of Bioinformatics and Medicine – in particular. Compared are inductive versus transductive reasoning, global, local, and personalised modeling, and all these approaches are illustrated on a case study of gene expression and clinical data related to cancer outcome prognosis. While inductive modeling is used to develop a model (function) from data on the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. A new method – WWKNN (weighted distance, weighted variables K-nearest neighbors), and a framework for the integration of global, local and personalised models for a single input vector are proposed. Integration of data (e.g. clinical and genetic) and of models (e.g. global, local and personalised) for a better pattern discovery, adaptation and accuracy of the results, are the major points of the paper.

NFI: a neuro-fuzzy inference method for transductive reasoning

This paper introduces a novel neural fuzzy inference method-NFI for transductive reasoning systems. NFI develops further some ideas from DENFIS-dynamic neuro-fuzzy inference systems for both online and offline time series prediction tasks. While inductive reasoning is concerned with the development of a model (a function) to approximate data in the whole problem space (induction), and consecutively-using this model to predict output values for a new input vector (deduction), in transductive reasoning systems a local model is developed for every new input vector, based on some closest to this vector data from an existing database (also generated from an existing model). NFI is compared with both inductive connectionist systems (e.g., MLP, DENFIS) and transductive reasoning systems (e.g., K-NN) on three case study prediction/identification problems. The first one is a prediction task on Mackey Glass time series; the second one is a classification on Iris data; and the last one is a real medical decision support problem of estimating the level of renal function of a patient, based on measured clinical parameters for the purpose of their personalised treatment. The case studies have demonstrated better accuracy obtained with the use of the NFI transductive reasoning in comparison with the inductive reasoning systems.