mathematics

Věra Kůrková

Věra Kůrková is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Věra Kůrková rather than just read about it. In short: Věra Kůrková (born 1948) is a Czech mathematician and computer scientist, affiliated with the Institute of Computer Science of the Czech Academy of Sciences. Her research interests include neural networks, computational learning theory, and nonlinear approximation theory.

Key takeaways

Věra Kůrková belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Věra Kůrková to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Věra Kůrková from memory before moving on to harder problems.

Reference excerpt

Věra Kůrková (born 1948) is a Czech mathematician and computer scientist, affiliated with the Institute of Computer Science of the Czech Academy of Sciences. Her research interests include neural networks, computational learning theory, and nonlinear approximation theory. She formulated the abstract concept of a variational norm in 1997 which puts ideas of Maurey, Jones, and Barron into the context of functional analysis. See V. Kůrková, Dimension-independent rates of approximation by neural networks. In: Warwick, K., Karny, M. (eds.) Computer-Intensive Methods in Control and Signal Processing. The Curse of Dimensionality, Birkhauser, Boston, MA, pp. 261–270 (1997). See also F. Girosi and G. Anzellotti, Convergence rates of approximation by translates, MIT Artificial Intelligence Laboratory, AI Memo No. 1288, April 1995, C.B.I.P. Paper No. 73. Kůrková is also known for the concept of quasiorthogonal set which she developed jointly with Robert Hecht-Nielsen and Paul Kainen. Kůrková earned a Ph.D. in 1980 and a habilitation in 1999, both from Charles University. She has been affiliated with the Czech Academy of Sciences since 1990, and she headed the Department of Theoretical Computer Science within the Institute of Computer Science from 2002 to 2008. In 2010, the Czech Academy of Sciences awarded Kůrková the Bernard Bolzano Honorary Medal for Merit in the Mathematical Sciences. From 2017 to 2019, Kůrková was president of the European Neural Network Society. For recent work, see V. Kůrková, M. Sanguineti, Classification by sparse neural networks, IEEE Trans Neural Netw Learn Syst. 2019 Jan 10. doi: 10.1109/TNNLS.2018.2888517 [Epub ahead of print] and two chapters in the forthcoming Vladik Kreinovich Festschrift volume published by Springer.

References

Worked examples

Example 1 — a first encounter with Věra Kůrková

Start with the simplest possible case. Write down what Věra Kůrková claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Věra Kůrková before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Věra Kůrková ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Věra Kůrková

In research: Věra Kůrková appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Věra Kůrková in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Věra Kůrková is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1948 births, Charles University alumni, Czech mathematicians, so understanding it makes those chapters shorter.
In everyday life: Look for Věra Kůrková outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Věra Kůrková in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Věra Kůrková means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Věra Kůrková out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Věra Kůrková in simple terms?

Why does Věra Kůrková matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Věra Kůrková?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Věra Kůrková.