CLeaR: An adaptive continual learning framework for regression tasks

Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework’s performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.

CLeaR: An Adaptive Continual Learning Framework for Regression Tasks

Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.

Implementasi Jaringan Syaraf Tiruan Pada Kendali Lampu Sorot Mobil Adaptif Berbasis Python

Fungsi utama dari lampu jalan untuk memastikan keamaan manusia. Penerangan lalu lintas diharuskan memberikan kondisi visibilitas yang baik dan mengurangin potensi bahaya dengan menerangi objek di sepanjang jalan. Jaringan Syaraf Tiruan diharapkan menghasilkan model terbaik untuk mengendalikan intensitas lampu sorot mobil adaptif pada kondisi yang sesuai dengan lapangan yaitu kondisi terang, mendung dan malam hari. Data diperoleh dari alat bantu yang terdiri dari 5 buah sensor cahaya dan 2 buah LED. Model terbaik didapat melalui training beberapa bentuk model Jaringan Syaraf Tiruan dan prediksi intensitas cahaya lampu sorot mobil berdasarkan dataset training dan testing. Training dilakukan pada 12 model berbeda dengan merubah banyak neuron hidden layer dan fungsi aktivasi pada program Jaringan Syaraf Tiruan. Model Jaringan Syaraf Tiruan terbaik memiliki parameter 20 node hidden layer, fungsi aktivasi Relu dan epoch 200 dengan error training sebesar 0,0038 dan hasil error prediksi sebesar 147,12.

Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning

Extraction of teeth is an important treatment decision in orthodontic practice. An expert system that is able to arrive at suitable treatment decisions can be valuable to clinicians for verifying treatment plans, minimizing human error, training orthodontists, and improving reliability. In this work, we train a number of machine learning models for this prediction task using data for 287 patients, evaluated independently by five different orthodontists. We demonstrate why ensemble methods are particularly suited for this task. We evaluate the performance of the machine learning models and interpret the training behavior. We show that the results for our model are close to the level of agreement between different orthodontists.

Effectiveness of training and capacity building methodologies with references to supporting staffs ofHealth care Industries in Tamil Nadu, India

Training is an experience of learning in that it seeks a relatively changes in an individual that will improve their activity to perform on the job. It involves the changing of skills, knowledge, attitudes and/orbehaviour. It may mean changing what employees know, how they work, their attitudes toward their work, or their interaction with their co-workers or supervisor. Training and capacity building programmes helps to increase the knowledge and skills of employees for performing better in a particular job. The major output of training and capacity building programmes are learning and application into the current job and assigned work. The effective training and capacity building programmes offerthe new habits, refined skills and useful knowledge during the training period that will help him/her to improve the performance. Learning experience of a training and capacity building programme that is properly planned and carried out by the organization to enable more skilled task based behaviour by the trainee. Training and capacity building programme provides ability to detect and correct error. Training provides skilland ability that may lie called on the current and future to satisfy the needs of human resources of the organization.

Effectiveness of training and capacity building methodologies with references to supporting staffs ofHealth care Industries in Tamil Nadu, India

Training is an experience of learning in that it seeks a relatively changes in an individual that will improve their activity to perform on the job. It involves the changing of skills, knowledge, attitudes and/orbehaviour. It may mean changing what employees know, how they work, their attitudes toward their work, or their interaction with their co-workers or supervisor. Training and capacity building programmes helps to increase the knowledge and skills of employees for performing better in a particular job. The major output of training and capacity building programmes are learning and application into the current job and assigned work. The effective training and capacity building programmes offerthe new habits, refined skills and useful knowledge during the training period that will help him/her to improve the performance. Learning experience of a training and capacity building programme that is properly planned and carried out by the organization to enable more skilled task based behaviour by the trainee. Training and capacity building programme provides ability to detect and correct error. Training provides skilland ability that may lie called on the current and future to satisfy the needs of human resources of the organization.

Comparing continual task learning in minds and machines

Humans can learn to perform multiple tasks in succession over the lifespan (“continual” learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion. Analysis of human error patterns suggested that blocked training encouraged humans to form “factorized” representation that optimally segregated the tasks, especially for those individuals with a strong prior bias to represent the stimulus space in a well-structured way. By contrast, standard supervised deep neural networks trained on the same tasks suffered catastrophic forgetting under blocked training, due to representational interference in the deeper layers. However, augmenting deep networks with an unsupervised generative model that allowed it to first learn a good embedding of the stimulus space (similar to that observed in humans) reduced catastrophic forgetting under blocked training. Building artificial agents that first learn a model of the world may be one promising route to solving continual task performance in artificial intelligence research.