Mar 30, 2021 · The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research.

Exploratory Research • Defining new problems, new constraints, new opportunities, new approaches • Example: • Multiple-Instance Learning: Labeled bags of instances • Adversarial …

May 1, 2019 · To demystify machine learning and to offer a learning path for those who are new to the core concepts, let’s look at ten different methods, including simple descriptions, …

Evaluation methodology (1) Standard methodology: 1. Collect large set of examples with correct classifications (aka ground truthdata) 2. Randomly divide collection into two disjoint sets: …

In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application.

Machine learning techniques comprise an array of computer-intensive methods that aim at discovering patterns in data using flexible, often nonparametric, methods for modeling and …

Oct 25, 2024 · Data science often employs methods such as machine learning, AI, natural language processing, algorithms, and other analytic tools to process and understand data. Big …

Oct 20, 2021 · The main focus of this presentation is to present ‘Machine learning' methods, which can be defined as algorithms that learn patterns (a function) about a particular …

Jan 15, 2020 · This entry is an overview of machine learning methods for social science research. It covers supervised learning methods including generalized linear models, support vector …

Experiments play an increasing role in machine learning research. The prototypical experi- mental paradigm is to measure the generalization error of a model on a benchmark dataset, and, …