Machine learning paradigms

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures.

14p vibransone 28-03-2024 2 2   Download

In this paper, a damage detection methodology for steel frame structures under fire load using time-history acceleration and machine learning (ML) is proposed. A randomly created dataset by finite element analysis (FEA) is utilized to develop deep neural networks (DNNs).

5p vicwell 06-03-2024 7 3   Download

Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine. While there were efforts to build radioresponse signatures using OMICS data, their ability to accurately predict in patients is still limited.

9p vimahuateng 26-11-2021 8 1   Download

Lecture Advanced Computer Networks - Chapter 12: Machine Learning for Networking. After studying this section will help you understand: final bonus assignment signup; ML algorithms are used in networking problems and how to apply reinforcement learning to decision making in networking problems,...

42p bachdangky 31-08-2021 5 2   Download

Over the past few decades, advanced analytics techniques, such as data analytics, data mining, and machine learning have attracted the attention of analysts, data scientists, researchers, and engineers in various fields in order to exploit very large and diverse data sets.

11p visumika2711 17-07-2019 5 0   Download

Chapter 1: Overview on Pattern Recognition and Machine Learning includes about Pattern Recognition, Machine learning, Related fields of pattern recognition, Classification, Two paradigms of pattern recognition.

18p cocacola_10 08-12-2015 39 3   Download

Translation needs have greatly increased during the last years. In many situations, text to be translated constitutes an unbounded stream of data that grows continually with time. An effective approach to translate text documents is to follow an interactive-predictive paradigm in which both the system is guided by the user and the user is assisted by the system to generate error-free translations. Unfortunately, when processing such unbounded data streams even this approach requires an overwhelming amount of manpower. ...

10p bunthai_1 06-05-2013 46 3   Download

In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a number of semi-supervised methods have been proposed, their effectiveness on NLP tasks is not always clear. This paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning.

9p bunbo_1 17-04-2013 53 2   Download

We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classification problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem. To demonstrate the applicability of the algorithms, we evaluated them on a number of classification tasks related to incremental dependency parsing. ...

6p hongvang_1 16-04-2013 58 4   Download

As researchers seek to apply their machine learning algorithms to new problems, corpus annotation is increasingly gaining importance in the NLP community. But since the community currently has no general paradigm, no textbook that covers all the issues (though Wilcock’s book published in Dec 2009 covers some basic ones very well), and no accepted standards, setting up and performing small-, medium-, and large-scale annotation projects remains something of an art. To attend, no special expertise in computation or linguistics is required. ...

1p hongdo_1 12-04-2013 42 3   Download

Even since computers were invented many decades ago, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both traditional Artificial Intelligence (AI) and emerging Cognitive Science.

386p bi_bi1 11-07-2012 85 21   Download