feature engineering

One fundamental principle of deep learning is to do away with hand-crafted feature engineering and to use raw features.

Selecting and designing good features is an important area in machine learning, which is called feature engineering.

The best results are achieved when an expert constructs a set of application-dependent features, a process called feature engineering.

Feature engineering is an informal topic, but it is considered essential in applied machine learning.

Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work.

The need for manual feature engineering can be obviated by automated feature learning.

Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive.

Extracting or selecting features is a combination of art and science; developing systems to do so is known as feature engineering.

The Gathering is the first of five albums to date featuring engineering and mixing work done with artist Andy Sneap.

Using this declarative framework frees the developer from low level feature engineering while capturing the problem's domain-specific properties and guarantying exact inference.

Here you will find the two projects included in our Mar/Apr 2002 issue which featured Engineering Feats & Failures.

The album features engineering work by Jim O'Rourke, and guitar and vocals by Bill Callahan.

- A fish-shaped robot and propeller-driven helium balloons competed for the title of fastest, lightest flying robot in an event featuring engineering students from all over Asia.

It was designed by NBBJ and Magnusson Klemencic Associates with future expansion in mind and features engineering designed to withstand earthquakes and other possible threats.

For supervised learning tasks, deep learning methods obviate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures which remove redundancy in representation.

This obviates manual feature engineering, which is otherwise necessary, and allows a machine to both learn at a specific task (using the features) and learn the features themselves: to learn how to learn.

Hitendra Zhangada, in the SPS Common Software Features Engineering group, at Sun described a variety of PCIe parameters in software which support the hardware platforms.

Hitendra Zhangada, SPS Common SW Features Engineering, at Software Group, Sun Microsystems, Inc. wrote an email on December 9 in 2008 sponsoring a fast-track software ARC case describing Supernova platforms AT480 and AT880.