Using Machine Learning to Understand and Influence Human Categorization Behavior PDF Download
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 220
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Book Description
In both machine learning (ML) and cognitive psychology (CP), categorization is considered a basic task commonly encountered by learning agents and studied in both fields. While a great deal of work in CP has been applied to understanding human learning in supervised categorization, little work has been done previously to investigate the effects of both labeled and unlabeled data as in the semi-supervised setting. I have had the opportunity to contribute to a number of studies investigating just this situation: human learners tasked with learning a categorization task from some combination of labeled and unlabeled data. This work has involved the use of ML to both (1) better understand how labeled and unlabeled data affect human learners in categorization tasks as well as (2) attempt to influence the resulting behavior using ideas and techniques derived from ML. The results of this work have shown that (1) in addition to humans being affected by the distribution of unlabeled data, they can also be affected by ordering of the unlabeled items (2) that humans are not constrained in their search of a parameter space when attempting to integrate new unlabeled items with previously labeled experience (3) that humans can learn using underlying manifold structure (4) that the speed of human learning on a supervised task can be affected by prior unlabeled experience and (5) that, using Co-Training constraints, human collaborators can be induced to learn a boundary neither would have likely learned on their own.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 220
Get Book Here
Book Description
In both machine learning (ML) and cognitive psychology (CP), categorization is considered a basic task commonly encountered by learning agents and studied in both fields. While a great deal of work in CP has been applied to understanding human learning in supervised categorization, little work has been done previously to investigate the effects of both labeled and unlabeled data as in the semi-supervised setting. I have had the opportunity to contribute to a number of studies investigating just this situation: human learners tasked with learning a categorization task from some combination of labeled and unlabeled data. This work has involved the use of ML to both (1) better understand how labeled and unlabeled data affect human learners in categorization tasks as well as (2) attempt to influence the resulting behavior using ideas and techniques derived from ML. The results of this work have shown that (1) in addition to humans being affected by the distribution of unlabeled data, they can also be affected by ordering of the unlabeled items (2) that humans are not constrained in their search of a parameter space when attempting to integrate new unlabeled items with previously labeled experience (3) that humans can learn using underlying manifold structure (4) that the speed of human learning on a supervised task can be affected by prior unlabeled experience and (5) that, using Co-Training constraints, human collaborators can be induced to learn a boundary neither would have likely learned on their own.
Author: Chi-Chun Lee
Publisher: CRC Press
ISBN: 1000794180
Category : Science
Languages : en
Pages : 130
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Book Description
The main purpose of the work is to showcase the interdisciplinary engineering approaches in modeling and understanding human behaviors during interpersonal interactions those that could be typical, distressed, or atypical. The ability to measure human behaviors quantitatively has been a core component and a major research direction in both fields of engineering and psychology – though often with distinct approaches designed for different targeted applications. Engineering methods often strive to achieve high predictive accuracies using behavioral informatics techniques; these techniques employ a combination of behavior measures derived using automated signal based descriptors, and of statistical frameworks modeled using machine learning techniques. These approaches are often distinct from the observational approaches the gold standard for the past three decades in the study of psychology, even in clinical settings. The observational approaches are largely based on human subjective judgments.
Author: Enrique Garcia Ceja
Publisher: CRC Press
ISBN: 1000484238
Category : Psychology
Languages : en
Pages : 434
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Book Description
Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320
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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Rai, Mritunjay
Publisher: IGI Global
ISBN:
Category : Psychology
Languages : en
Pages : 332
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Book Description
In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.
Author: Mohamed Chetouani
Publisher: Springer
ISBN: 331946843X
Category : Computers
Languages : en
Pages : 164
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Book Description
This book constitutes the refereed proceedings of the 7th International Workshop on Human Behavior Understanding, HBU 2016, held in Amsterdam, The Netherlands, in October 2016. The 10 full papers were carefully reviewed and selected from 17 initial submissions. They are organized in topical sections named: behavior analysis during play; daily behaviors; gesture and movement analysis; and vision based applications.
Author: Sílvio Manuel Brito
Publisher: BoD – Books on Demand
ISBN: 1839622431
Category : Education
Languages : en
Pages : 164
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Book Description
Active learning is now a form of learning that accompanies the knowledge evolution that challenges the learner to promote it, but also encourages him to investigate and become emotionally involved in the task. The great key to obtaining this behavior successfully depends, therefore, on the subject's involvement and ability to undertake, so that active learning becomes emotional entrepreneurial learning that generates new ideas and new forms of knowledge. From memorization, we move on to inquiry, from questioning to constructive participation, from hypostasis to problem-solving, from generalization to critical thinking. When we look at this book, we see real examples, concrete, and senses, from the most important act of human nature: learning!
Author: Albert Ali Salah
Publisher: Springer Science & Business Media
ISBN: 3642254454
Category : Computers
Languages : en
Pages : 166
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Book Description
This book constitutes the refereed proceedings of the Second International Workshop on Human Behavior Understanding, HBU 2011, held in Amsterdam, The Netherlands, in November 2011, in conjunction with AmI-11, the International Joint Conference on Ambient Intelligence. The 13 revised full papers presented together with 2 keynote talks and one summarizing paper were carefully reviewed and selected from 32 submissions. The papers are organized in topical sections on analysis of human actions and activities, face and gesture analysis, persuasive technologies, and social interactions.
Author: Bogdan Ionescu
Publisher: Springer Nature
ISBN: 3030814653
Category : Computers
Languages : en
Pages : 297
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Book Description
Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.
Author: Claudia Yamu
Publisher: Routledge
ISBN: 1351981498
Category : Architecture
Languages : en
Pages : 288
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Book Description
The Virtual and the Real in Planning and Urban Design: Perspectives, Practices and Applications explores the merging relationship between physical and virtual spaces in planning and urban design. Technological advances such as smart sensors, interactive screens, locative media and evolving computation software have impacted the ways in which people experience, explore, interact with and create these complex spaces. This book draws together a broad range of interdisciplinary researchers in areas such as architecture, urban design, spatial planning, geoinformation science, computer science and psychology to introduce the theories, models, opportunities and uncertainties involved in the interplay between virtual and physical spaces. Using a wide range of international contributors, from the UK, USA, Germany, France, Switzerland, Netherlands and Japan, it provides a framework for assessing how new technology alters our perception of physical space.
