Geoffrey Hinton은 Andrew Ng과의 인터뷰에서 지금까지의 연구자로써의 인생에서 가장 자랑스러운 것을 꼽으라고 한다면 RBM을 이용한 연구성과를 꼽는다고 말한 바 있다. RBM을 이용해서 Hinton이 기여한 것은 딥 뉴럴네트워크의 초기화가 얼마나 중요한 것인지 밝혀낸 것이다. 어떻게 초기화를 하는지가 딥 뉴럴네트워크가 효과적으로 학습되는지의 성패를 좌우할 수 있기 때문이다. RBM은...
It is not etched in stone that relationships between factor exposures and returns must be linear. We found machine-learning algorithms could identify nonlinear relationships and be used to construc...
Discover essential factors to consider when selecting a camera for industrial machine vision in our informative guide.
The goal of this Research Topic is to invite innovative papers on machine learning and factor pricing with applications in finance. Examples of the types of questions we seek to answer...
The results suggest that machine learning increases the value of assets based on parts, as proved by the findings of [4], and machine learning can enhance factor-based portfolio expansion...
We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted...
In anomaly detection, the local outlier factor ( LOF ) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours. LOF shar...
use machine learning techniques to automatically detect... NLP-based machine learning. However, practitioners lack... representative machine learning models impact the accuracy of...
Port of the Erlang webmachine to Factor. Contribute to x6j8x/factor-machine development by creating an account on GitHub.
Factor implicit thought in factor space theory can be used to solve the classification problem of machine learning. On the basis of factor implicitness, a serial sweeping class classification algor...