Step 7

"If I have seen further, it is because I have stood on the shoulders of giants", – Isaac Newton wrote this in a letter in 1675.

We establish a solid foundation for our research to deliver results of better quality and higher impact with less effort. This foundation comprises essentials, overviews, state-of-the-art, and alternatives to our solution. Not to mention the supplementary materials like references to the algorithms and data in your computational experiment.

The seminar

1. The warm-up 5-minute test
2. The expert estimations and integral indicators
3. Introduction; strengths and weaknesses of basic solutions
4. Make a table of them on your homework
5. Dicsuss the project comparison

Resources

Step 7 YouTube []

Homework

Each solution to your problem has its pros and cons. List them all.

1. Collect references for your project according to the plan.
2. While collecting put your notes to the temporary file LinkReview (see examples below) or to the .bib and .tex
3. Put them into the .bib using JabRef or another tool that generates the bibliographic record using doi.
4. Analyse each solution's pros and cons according to the project's quality criteria.
5. Put the short version of your analysis in the table.
6. Fill in the table in your repository.
7. Put the link to your analysis here, Step-7.

While completing the table, remember the formula for an engineering project description: "We propose a solution that offers a unique feature, distinguishing it from other solutions." Of course, each part needs an explanation.

Note: The whole section Introduction is not required.

Examples

1. Look through the table of reports and see some particular LinkReview files.
2. Examples of the comparative tables
   1. Generation of simple structured IR functions by genetic algorithm without stagnation
   2. Feature Selection for ECoG-based Brain-Computer Interface
3. See parts of Introduction (to put it in the LinkReview as a draft)
   1. Related works: Stresstest procedures for feature selection algorithms
   2. The main contributions of this paper are: Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria
4. For the future materials see a problem statement (beware the spherical horse in a vacuum)