scholarly journals Towards Knowledgeable Supervised Lifelong Learning Systems

2020 ◽  
Vol 68 ◽  
pp. 159-224
Author(s):  
Diana Benavides-Prado ◽  
Yun Sing Koh ◽  
Patricia Riddle
Keyword(s):  
Machine Learning ◽  
Multitask Learning ◽  
Learning Systems ◽  
Learning System ◽  
Support Vector ◽  
Vector Machines ◽  
Real World Datasets ◽  
First Time ◽  
Continual Learning

Learning a sequence of tasks is a long-standing challenge in machine learning. This setting applies to learning systems that observe examples of a range of tasks at different points in time. A learning system should become more knowledgeable as more related tasks are learned. Although the problem of learning sequentially was acknowledged for the first time decades ago, the research in this area has been rather limited. Research in transfer learning, multitask learning, metalearning and deep learning has studied some challenges of these kinds of systems. Recent research in lifelong machine learning and continual learning has revived interest in this problem. We propose Proficiente, a full framework for long-term learning systems. Proficiente relies on knowledge transferred between hypotheses learned with Support Vector Machines. The first component of the framework is focused on transferring forward selectively from a set of existing hypotheses or functions representing knowledge acquired during previous tasks to a new target task. A second component of Proficiente is focused on transferring backward, a novel ability of long-term learning systems that aim to exploit knowledge derived from recent tasks to encourage refinement of existing knowledge. We propose a method that transfers selectively from a task learned recently to existing hypotheses representing previous tasks. The method encourages retention of existing knowledge whilst refining. We analyse the theoretical properties of the proposed framework. Proficiente is accompanied by an agnostic metric that can be used to determine if a long-term learning system is becoming more knowledgeable. We evaluate Proficiente in both synthetic and real-world datasets, and demonstrate scenarios where knowledgeable supervised learning systems can be achieved by means of transfer.

scholarly journals Methods for the Explanation of Machine Learning Processes and Results for Non-Experts

2018 ◽  
Author(s):  
Joachim Diederich
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Learning System ◽  
Support Vector ◽  
Domain Expert ◽  
First Order ◽  
Science Technology ◽  
Expert User ◽  
Vector Machines ◽  
Multi Media

Machine learning systems such as artificial neural networks (ANNs) and support vector machines (SVMs) are commonplace in science, technology, business and health. However, the lack of an explanation capability is an impediment to the more wide-spread use of machine learning systems. For more than 20 years, the field of rule extraction from ANNs and SVMs has tried to generate explanations for machine learning based decisions in the form of propositional, probabilistic or first-order rules. However, there is no generally accepted technique that works for all machine learning systems. In addition, there is no method that is tailored to the needs of a user who may not be a domain expert and may not have a technology background. This paper introduces a number of techniques for the generation of multi-media clips that explain the behavior of a machine learning system to a non-expert user.

scholarly journals Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data

2019 ◽  
Vol 11 (10) ◽  
pp. 1195 ◽  
Author(s):  
Minsang Kim ◽  
Myung-Sook Park ◽  
Jungho Im ◽  
Seonyoung Park ◽  
Myong-In Lee
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Surface Wind ◽  
Support Vector ◽  
Learning Approaches ◽  
Yearly Variation ◽  
Linear Discriminant ◽  
Forecast Lead Time ◽  
Vector Machines ◽  
First Time

This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005–2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21–28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26–30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches.

scholarly journals Gaze Tracking in Semi-Autonomous Grasping

2008 ◽  
Vol 2 (4) ◽  
Author(s):  
Claudio Castellini
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Human Subjects ◽  
Learning System ◽  
Support Vector ◽  
Hand Position ◽  
Human Being ◽  
Gaze Tracking ◽  
Vector Machines ◽  
On Line

In critical human/robotic interactions such as, e.g., teleoperation by a disabled master or with insufficient bandwidth, it is highly desirable to have semi-autonomous robotic artifacts interact with a human being. Semi-autonomous grasping, for instance, consists of having a smart slave able to guess the master’s intentions and initiating a grasping sequence whenever the master wants to grasp an object in the slave’s workspace. In this paper we investigate the possibility of building such an intelligent robotic artifact by training a machine learning system on data gathered from several human subjects while trying to grasp objects in a teleoperation setup. In particular, we investigate the usefulness of gaze tracking in such a scenario. The resulting system must be light enough to be usable on-line and flexible enough to adapt to different masters, e.g., elderly and/or slow. The outcome of the experiment is that such a system, based upon Support Vector Machines, meets all the requirements, being (a) highly accurate, (b) compact and fast, and (c) largely unaffected by the subjects’ diversity. It is also clearly shown that gaze tracking significantly improves both the accuracy and compactness of the obtained models, if compared with the use of the hand position alone. The system can be trained with something like 3.5 minutes of human data in the worst case.task is neutral.

scholarly journals Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

2020 ◽  
Author(s):  
Roberto Sánchez-Reolid ◽  
María T. López ◽  
Antonio Fernández-Caballero
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
High Performance ◽  
Electrodermal Activity ◽  
Support Vector ◽  
Stress Detection ◽  
Scientific Databases ◽  
Vector Machines ◽  
Detection Of Stress

Early detection of stress can prevent us from suffering from a long-term illness such as depression and anxiety. This article presents a scoping review of stress detection based on electrodermal activity (EDA) and machine learning (ML). From an initial set of 395 articles searched in six scientific databases, 58 were finally selected according to various criteria established. The scoping review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, preprocessing, processing and feature extraction. Finally, all the ML techniques applied to the features of this signal have been studied for stress detection. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high performance values. On the contrary, it has been evidenced that unsupervised learning is not very common in the detection of stress through EDA. This scoping review concludes that the use of EDA for the detection of arousal variation (and stress detection) is widely spread, with very good results in its prediction with the ML methods found during this review.

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

scholarly journals Identification of the Rumination in Cattle Using Support Vector Machines with Motion-Sensitive Bolus Sensors

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1165 ◽  
Author(s):  
Andrew Hamilton ◽  
Chris Davison ◽  
Christos Tachtatzis ◽  
Ivan Andonovic ◽  
Craig Michie ◽  
...  
Keyword(s):  
Ph Value ◽  
Support Vector ◽  
Individual Animal ◽  
Digestive Efficiency ◽  
Ruminal Ph ◽  
Motion Energy ◽  
Vector Machines ◽  
First Time ◽  
Low Activity ◽  
Poor Welfare

The reticuloruminal function is central to the digestive efficiency in ruminants. For cattle, collar- and ear tag-based accelerometer monitors have been developed to assess the time spent ruminating on an individual animal. Cattle that are ill feed less and so ruminate less, thus, the estimation of the time spent ruminating provides insights into the health of individual animals. pH boluses directly provide information on the reticuloruminal function within the rumen and extended (three hours or more) periods during which the ruminal pH value remains below 5.6 is an indicator that dysfunction and poor welfare are likely. Accelerometers, incorporated into the pH boluses, have been used to indicate changes in behaviour patterns (high/low activity), utilised to detect the onset of oestrus. The paper demonstrates for the first time that by processing the reticuloruminal motion, it is possible to recover rumination periods. Reticuloruminal motion energy and the time between reticuloruminal contractions are used as inputs to a Support Vector Machine (SVM) to identify rumination periods with an overall accuracy of 86.1%, corroborated by neck mounted rumination collars.

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