Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Travis Greene, Galit Shmueli
Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of…
Louisa Cornelis, Guillermo Bernárdez, Haewon Jeong, Nina Miolane
Personalized Models Authors: ['Louisa Cornelis' 'Guillermo Bernárdez' 'Haewon Jeong' 'Nina Miolane'] Personalization in machine learning involves tailoring models to individual users by incorporating personal attributes such as demographic or medical data. While personalization can improve prediction accuracy, it may…
Andrew Coristine, Grzegorz Bulaj, Beibin Li, Weilin Xu + 1 more
'Peter Washington'] Digital health interventions often use machine learning (ML) models to make predictions of repeated adverse health events. For example, models may be used to analyze patient data to identify patterns that can anticipate the likelihood of disease exacerbations, enabling timely interventions and…
Ali Kargarandehkordi, Matti Kaisti, Peter Washington
Our study delves into the concept of model personalization in emotion recognition, moving away from the one-size-fits-all approach. We conducted a series of experiments using the Emognition dataset, comprising physiological and video data of human subjects expressing various emotions, to investigate a personalized…
Pekka Siirtola, Juha Röning
This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application, personal streaming data can be gathered. Of course, this data does…
Yanyan Luo, Xue Han, Ruiqiao Bai, Xin Huang + 9 more
Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. However, the mechanisms that enable personalization also expand the safety landscape in ways not systematically addressed by existing literature. Existing reviews…
Nat Roth, Justin Wagle
We introduce a new metric for measuring how well a model personalizes to a user's specific preferences. We define personalization as a weighting between performance on user specific data and performance on a more general global dataset that represents many different users. This global term serves as a form of…
Stefano Canali, Alessandro Falcetta, Massimo Pavan, Manuel Roveri + 1 more
The use of big data and machine learning has been discussed in an expanding literature, detailing concerns on ethical issues and societal implications. In this paper we focus on big data and machine learning in the context of health systems and with the specific purpose of personalization. Whilst personalization is…
Jialin Li, Alia Waleed, Hanan Salam
—In computing, the aim of personalization is to train a model that caters to a specific individual or group of people by optimizing one or more performance metrics and adhering to specific constraints. In this paper, we discuss the need for personalization in affective and personality computing (hereinafter referred to…
Eduardo Vicente-López, Luis M. de Campos, Juan M. Fernández‐Luna, Juan F. Huete
'Juan F. Huete'] Abstract Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We…
Lorraine Buis, Li Xiong, Christoph Friedrich, Jessica Chia Liu + 3 more
Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a…
Han Yu, Chunyan Miao, Cyril Leung, Timothy John White
Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge…
Ramyaa Ramyaa, Omid Hosseini, Giri P Krishnan, Sridevi Krishnan
Nutritional phenotyping is a promising approach to achieve personalized nutrition. While conventional statistical approaches haven’t enabled personalizing well yet, machine-learning tools may offer solutions that haven’t been evaluated yet. The primary aim of this study was to use energy balance components – input…
Arni S.R. Srinivasa Rao, Michael P. Diamond
In this technical article, we are proposing ideas those we have been developing of how machine learning and deep learning techniques can potentially assist obstetricians / gynecologists in better clinical decision making using infertile women in their treatment options in combination with mathematical modeling in…
Authors not listed
Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
Qiang Gu, Anup Kumar, Simon Bray, Allison Creason + 4 more
Supervised machine learning, where the goal is to predict labels of new instances by training on labeled data, has become an essential tool in biomedical data analysis. To make supervised machine learning more accessible to biomedical scientists, we have developed Galaxy-ML, a platform that enables scientists to…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Kaushal Kumar, Vijay Vikram Singh, Ramakrishna Ramaswamy
Machine learning (ML) plays a key job in the guide of cancer diagnosis and identification. The researcher has implemented different algorithms of ML for the prediction of breast cancer. Some researchers recommend their algorithms are more accurate, faster, and easier than others. My study relies on recently developed…
Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu + 3 more
Sepsis is a major health crisis in US hospitals, and several clinical identification systems have been designed to help care providers with early diagnosis of sepsis. However, many of these systems demonstrate low specificity or sensitivity, which limits their clinical utility. We evaluate the effects of a machine…