scholarly journals Learning function from structure in neuromorphic networks

2021 ◽  
Author(s):  
Laura E. Suárez ◽  
Blake A. Richards ◽  
Guillaume Lajoie ◽  
Bratislav Misic
Keyword(s):  
Learning Function

Logics for Action and Conventional Wisdom

2018 ◽  
Author(s):  
Alastair Stark
Keyword(s):  
Conventional Wisdom ◽  
Policy Learning ◽  
Learning Function

This chapter examines the logics for action that inquiry actors bring into a lesson-learning episode. Logics for action is a term that describes the knowledge-related preferences that actors use in inquiries to make decisions. Analysis of the logics in these cases leads to three specific arguments. First, that political logics for action do not compromise inquiries in the ways which inquiry research currently suggests. Second, that public-managerial logics are essential to inquiry success in terms of policy learning. Finally, that legal-judicial logics need not necessarily lead to blaming and adversarial proceedings, which derail the lesson-learning function. These three arguments once again suggest that we need to rethink much of the conventional wisdom surrounding inquiries.

scholarly journals From eLearning to Digital Transformation: A Framework and Implications for L&D

2016 ◽  
Vol 9 (2) ◽  
pp. 27 ◽  
Author(s):  
Sabine Seufert ◽  
Christoph Meier
Keyword(s):  
Human Resource Development ◽  
Human Resource ◽  
Corporate Training ◽  
Resource Development ◽  
Digital Transformation ◽  
Learning Processes ◽  
Organizational Level ◽  
The Core ◽  
Learning Function ◽  
Business Engineering

<p class="Abstract">How can the learning function (L&amp;D) support learning and innovation at the level of an entire organization in times of digital transformation? The core challenges in this are twofold: 1) Competence clarification: What are relevant “digital competences” in terms of knowledge, skills and attitudes that employees need in order to cope with digital transformation? 2) Competence development: How to organize, design and support learning processes contributing to digital competences and digital transformation?</p>Building on a framework originating in the context of business engineering and applying it to corporate training and human resource development, we explicate what digital transformation implies for the L&amp;D function. As L&amp;D functions explore and exploit the options sketched out, they live digital transformation in a way that enables them to effectively and efficiently contribute to digital transformation at an organizational level.

scholarly journals Memory and learning function in juvenile delinquent youths diagnosed with PTSD

1999 ◽  
Vol 14 (8) ◽  
pp. 761-762
Author(s):  
T.B. Danforth ◽  
D.A. Gansler ◽  
R.A. McMackin
Keyword(s):  
Juvenile Delinquent ◽  
Memory And Learning ◽  
Learning Function

scholarly journals Warranty of Misinforming as an Option in Product Utilization Process

10.28945/3501 ◽  
2016 ◽  
Author(s):  
Dimitar Grozdanov Christozov ◽  
Stefanka Chukova ◽  
Plamen S. Mateev
Keyword(s):  
Risk Management ◽  
Smart Phones ◽  
Fixed Cost ◽  
Strike Price ◽  
Underlying Asset ◽  
Learning Function ◽  
Time To Learn ◽  
Definition Of ◽  
The Right

The following definition of “option” is given in Wikipedia - “In finance, an option is a contract which gives the buyer (the owner or holder) the right, but not the obligation, to buy or sell an underlying asset or instrument at a specified strike price on or before a specified date, depending on the form of the option.”. Option as a risk management (mitigation) tool is broadly used in finance and trades. At the same time it introduces asymmetry in the sense that, probabilistically, it limits the level of loses (i.e., the price of option) and allows for unlimited gains. In the market of sophisticate devices (as smart phones, tablets, etc.), where technologies are rapidly advancing, customers usually do not have the experience to use all features of the device at the time of purchasing. Due to the lack of appropriate expertise, the risk of misinforming leading to not purchasing the "right" device is high, but given enough time to learn the capabilities of the device and map them to the problems faced could provide the client with substantial long term benefits. Warranty of misinforming is the mechanism to provide the client with the opportunity to explore the device and master its features with a limited risk of loses. Thus, the warranty of misinforming could be considered as an option - the customers buys it (at a fixed cost) and may gain (theoretically) unlimited benefit by realizing (within the warranty) that the device can be used to solve variety of problems not considered at the purchase time. The paper investigates the learning function of warranty of misinforming, when used as an option.

scholarly journals Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions

2019 ◽  
Author(s):  
Emir Demirovic ◽  
Peter J. Stuckey ◽  
James Bailey ◽  
Jeffrey Chan ◽  
Christopher Leckie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Combinatorial Optimisation ◽  
Linear Functions ◽  
Optimal Parameters ◽  
Common Goal ◽  
Learning Function ◽  
Input Parameters ◽  
Theoretical Insight ◽  
Insight Into

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our contributions are two-fold: 1) we provide theoretical insight into the properties and computational complexity of predict+optimise problems in general, and 2) develop a novel framework that, in contrast to related work, guarantees to compute the optimal parameters for a linear learning function given any ranking optimisation problem. We illustrate the applicability of our framework for the particular case of the unit-weighted knapsack predict+optimise problem and evaluate on benchmarks from the literature.

scholarly journals Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

2019 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Hijratul Aini ◽  
Haviluddin Haviluddin
Keyword(s):  
Neural Network ◽  
Palm Oil ◽  
Good Accuracy ◽  
Activation Function ◽  
Crude Palm Oil ◽  
Backpropagation Neural Network ◽  
Neural Network Approach ◽  
Learning Function ◽  
Hidden Layer ◽  
Accuracy Of Prediction

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.

Sign in / Sign up

Export Citation Format

Share Document