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This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?”

This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project.

The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system.

The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

The purpose of this paper is to investigate the applicability of meta‐learning to the problem of algorithm recommendation for gene expression data classification.

Meta‐learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: k‐nearest neighbors and support vector machine‐based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta‐learning literature.

Empirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed.

As the experiments conducted in this paper suggest, the use of meta‐learning approaches can provide an efficient data driven way to select algorithms for gene expression data classification.

This paper reports contributions to the areas of meta‐learning and gene expression data analysis. Regarding the former, it supports the claim that meta‐learning can be suitably applied to problems of a specific domain, expanding its current practice. To the latter, it introduces a cost effective approach to better deal with classification tasks.

The purpose of this paper is to investigate the current glocal (global and local) environment to answer the following research questions: How does the glocal environment influence software exporting industries in India? How is the evolving “sticky” knowledge from individuals and teams assimilated into organizational knowledge repositories? What management practices have been learnt and applied for advancement of knowledge portfolios in the offshore software business market?

An interpretivist research design is used to gain insights into organizational learning processes adopted by offshore software vendors for assimilating evolving knowledge into knowledge repositories.

This paper describes the influence of the current glocal environment on software exporting industries in India and presents a model for organizational learning to assimilate knowledge and build effective representations of emerging knowledge artifacts. The authors employ the concept of meta‐learning (or “learning about learning”) to analyze the recursive nature of organizational learning processes.

The proposed model of meta‐learning explains how software organizations build on individual and team competencies to build core competencies. The model helps us to understand how organizations advance their learning processes and upgrade their knowledge repositories.

The paper offers new perspectives on how organizations reflexively monitor their knowledge processes to advance their knowledge portfolios. It identifies adhocratic and bureaucratic management processes for assimilating the evolving “sticky” knowledge from individuals into organizational knowledge repositories. This paper contributes to the growing body of literature that emphasizes ongoing learning from individual to collective level in the knowledge industry sector.

The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives.

Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models.

From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification.

This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

This paper is concerned with an online parameter estimation algorithm for nonlinear uncertain time‐varying systems for which no stochastic information is available.

The estimation procedure, called nonlinear learning rate adaptation (NLRA), computes an individual adaptive learning rate for each parameter instead of using a single adaptive learning rate for all the parameters as done in stochastic approximation, each individual learning rate being controlled by a meta‐learning rate rule for the sake of minimizing the measurement prediction error. The method does not require stochastic information about the system model and the measurement noise covariance matrices contrarily to the Kalman filtering. Numerical results about aircraft navigation trajectory tracking show that the method is able to estimate reliably time‐varying parameters even in presence of measurement noise.

The proposed algorithm is practically insensitive to changes in the meta‐learning rate. Therefore, the performance of the method is stable with respect to the tuning parameter of the algorithm.

The proposed NLRA method may be adopted for recursive parameter estimation of uncertain systems when no stochastic information is available. It may also be used for process regulation and dynamic system stabilization in feedback control applications.

Provides a method for fast and practical computation of parameter estimates without requiring to know the model and measurement noise covariance matrices contrarily to existing stochastic estimation methods.

Organizational learning capabilities are embedded in organizational communication systems and processes related to knowledge creation and articulation. The emergence of new organizational forms (such as horizontal organizations) in rapidly‐changing environments and hyper‐competitive markets underscores the need to better understand these foundational sources of learning. In fact, the reason horizontal organizations may find success is that their structure is intended to promote communications systems and processes which enhance a knowledge‐response sequence similar to a stimulus‐response sequence associated with learning. These systems permit managers to quickly gather information, respond with agility in making decisions, and continue to make ongoing adjustments. Firms which understand the need to build their communications capabilities may be characterized as meta‐learning organizations. Resource‐based theory suggests that communications systems and processes are thus sources of competitive advantage. Future empirical research on organizational learning may progress by evaluating specific measures of communication process as proxies for learning processes.

In this paper, we suggest a contemporary view of learning during the process of organizational socialization. The relationship between learning and socialization is implicit in much of the existing socialization literature. In an attempt to make this research more explicit, we suggest a theoretical approach to the actual learning processes that underlie workers’ socialization experiences. In order to accomplish this, we review previous work on socialization, information seeking and feedback seeking during socialization, and learning. In doing so we describe the learning process that underlies socialization, highlighting the beginning of the process, the role of information during the process, and integrating three different types of learning (planned, deutero, and meta) into the process of organizational socialization. In addition, we also discuss the implications of these three types of learning during the process of socialization and directions in future research on the socialization process.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

The purpose of this paper is to explore the field of learning, learning style, meta‐cognition, strategies and teaching by classifying different levels of the learning process. The paper aims to present an attempt to identify how students' awareness of learning style and teachers' matched instruction might affect students' learning and motivation.

The paper is a conceptual paper in which a theoretical framework built on empirical research was identified by connecting and systemizing different parts of the learning process.

The paper finds that teaching based on individual learning styles is an effective way to ensure students' achievement and motivation. Awareness of learning styles, it is argued, influences meta‐cognition and choice of relevant learning strategies. Consciousness of own improvement provides students with new perspectives of their learning potential. Such positive academic experiences may enhance self‐efficacy.

The paper provides useful information on unraveling concepts, methods and effects which can aid students, teachers and researchers in understanding, evaluating and monitoring learning, thus having practical implications for promoting lifelong learning, self‐efficacy and salutogenesis.