This paper reports work aimed at developing an open, distributed learning environment, OLLIE, where researchers can experiment with different Machine Learning (ML) methods for Information Extraction. Once the required level of performance is reached, the ML algorithms can be used to speed up the manual annotation process. OLLIE uses a browser client while data storage and ML training is perform...

statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. this paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. the thermal conductivity of nanofluids increases with the volume fraction and temperature. machine learni...

The subject of this course is automated learning, or, as we will more often use, machine learning (ML for short). Roughly speaking, we wish to program computers so that they can ”learn”. Before we discuss how machines can learn, or how the process of learning can be automated, let us consider two examples of naturally occurring animal learning. Not surprisingly, some of the most fundamental iss...

background the presence of an infected family member significantly increases the risk of hbv transmission, but many socio-demographic and viral characteristics of family members affect the transmission rate. objectives in this study, we have used data mining techniques to investigate the impact of different variables in intrafamilial transmission of hbv infection. patients and methods demograph...

Research in systems where learning is integrated to other components like problem solving, vision, or natural language is becoming an important topic for Machine Learning. Situations where learning methods are embedded or integrated into broader systems offers new theoretical challenges to ML and enlarge the potential range of ML applications. In this position paper we propose the research topi...

The main goal of this paper is to develop machine learning systems using fuzzy distributed artificial intelligent systems. The goal of machine learning is to ensemble learning and adaptation abilities of living species in computers; more deeply to program computers to use past experience to solve a given problem. As also stated by Michalski: “Learning is constructing or modifying representation...

The use of Machine Learning (ML) techniques is already widespread in Medicine Diagnosis. The use of these techniques helps increasing the efficiency of human diagnostic, which is significantly affected by the human conditions such as stress as well as the lack of experience. In this paper, integration between two ML techniques casebased reasoning (CBR) and artificial neural network (ANN) is use...

BACKGROUND Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called...

Machine Learning (ML) has become a mature technology that is being applied to a wide range of business problems such as web search, online advertising, product recommendations, object recognition, and so on. As a result, it has become imperative for researchers and practitioners to have a fundamental understanding of ML concepts and practical knowledge of end-to-end modeling. This tutorial take...