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Related papers: A Computational Model for Logical Analysis of Data

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In designing an intelligent system that must be able to explain its reasoning to a human user, or to provide generalizations that the human user finds reasonable, it may be useful to take into consideration psychological data on what types…

Artificial Intelligence · Computer Science 2013-04-15 James E. Corter , Mark A. Gluck

In functional data analysis, replicate observations of a smooth functional process and its derivatives offer a unique opportunity to flexibly estimate continuous-time ordinary differential equation models. Ramsay (1996) first proposed to…

Methodology · Statistics 2024-06-27 Edward Gunning , Giles Hooker

Factor analysis, a classical multivariate statistical technique is popularly used as a fundamental tool for dimensionality reduction in statistics, econometrics and data science. Estimation is often carried out via the Maximum Likelihood…

Optimization and Control · Mathematics 2018-01-19 Koulik Khamaru , Rahul Mazumder

We begin the systematic study of decision problems for finitely generated groups given by a solution to their word problem. We relate this to the study of computable analysis on the space of marked groups. We point out that several distinct…

Group Theory · Mathematics 2025-01-15 Emmanuel Rauzy

Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this…

Data Analysis, Statistics and Probability · Physics 2018-03-22 John Harlim

Canonical correlation analysis (CCA) is a classic statistical method for discovering latent co-variation that underpins two or more observed random vectors. Several extensions and variations of CCA have been proposed that have strengthened…

Machine Learning · Computer Science 2023-12-22 Paris A. Karakasis , Nicholas D. Sidiropoulos

Informatics and technological advancements have triggered generation of huge volume of data with varied complexity in its management and analysis. Big Data analytics is the practice of revealing hidden aspects of such data and making…

Databases · Computer Science 2018-03-30 Bikram Karmakar , Indranil Mukhopadhyay

This extended abstract presents a logic, called Lp, that is capable of representing and reasoning with a wide variety of both qualitative and quantitative statistical information. The advantage of this logical formalism is that it offers a…

Artificial Intelligence · Computer Science 2013-04-08 Fahiem Bacchus

Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately such anomalies requires models to reason about various…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Soopil Kim , Sion An , Philip Chikontwe , Myeongkyun Kang , Ehsan Adeli , Kilian M. Pohl , Sang Hyun Park

Logarithmic transformation of the data has been recommended by the literature in the case of highly skewed distributions such as those commonly found in information science. The purpose of the transformation is to make the data conform to…

Information Retrieval · Computer Science 2009-11-19 Loet Leydesdorff , Stephen Bensman

An equational logic program is a set of directed equations or rules, which are used to compute in the obvious way (by replacing equals with ``simpler'' equals). We present static analysis techniques for efficient equational logic…

Logic in Computer Science · Computer Science 2007-05-23 Rakesh M. Verma

This paper presents a significant advancement in the estimation of the Composite Link Model within a penalized likelihood framework, specifically designed to address indirect observations of grouped count data. While the model is effective…

Methodology · Statistics 2025-12-16 Carlo G. Camarda , María Durbán

In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved…

Data Structures and Algorithms · Computer Science 2010-10-11 Michael W. Mahoney

This talk describes how a combination of symbolic computation techniques with first-order theorem proving can be used for solving some challenges of automating program analysis, in particular for generating and proving properties about the…

Programming Languages · Computer Science 2017-04-17 Laura Kovacs

Growth in both size and complexity of modern data challenges the applicability of traditional likelihood-based inference. Composite likelihood (CL) methods address the difficulties related to model selection and computational intractability…

Statistics Theory · Mathematics 2017-09-12 Zhendong Huang , Davide Ferrari

Statistics and Optimization are foundational to modern Machine Learning. Here, we propose an alternative foundation based on Abstract Algebra, with mathematics that facilitates the analysis of learning. In this approach, the goal of the…

Machine Learning · Computer Science 2025-02-28 Fernando Martin-Maroto , Nabil Abderrahaman , David Mendez , Gonzalo G. de Polavieja

The latent class model is a powerful tool for identifying latent classes within populations that share common characteristics for categorical data in social, psychological, and behavioral sciences. In this article, we propose two new…

Machine Learning · Computer Science 2023-10-31 Huan Qing

Clustering attempts to partition data instances into several distinctive groups, while the similarities among data belonging to the common partition can be principally reserved. Furthermore, incomplete data frequently occurs in many…

Machine Learning · Computer Science 2022-08-30 Miao Cheng , Xinge You

Extracting useful information from high-dimensional data is an important focus of today's statistical research and practice. Penalized loss function minimization has been shown to be effective for this task both theoretically and…

Statistics Theory · Mathematics 2009-09-03 Peng Zhao , Guilherme Rocha , Bin Yu

A novel approach for supervised classification analysis for high dimensional and flat data (more variables than observations) is proposed. We use the information of class-membership of observations to determine groups of observations…