computer science

Machine Learning (journal)

Machine Learning (journal) is a computer science 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 Machine Learning (journal) rather than just read about it. In short: Machine Learning is a peer-reviewed scientific journal, published since 1986. In 2001, forty editors and members of the editorial board of Machine Learning resigned in order to support the Journal of Machine Learning Research (JMLR), saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives.

Key takeaways

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

Reference excerpt

Machine Learning is a peer-reviewed scientific journal, published since 1986. In 2001, forty editors and members of the editorial board of Machine Learning resigned in order to support the Journal of Machine Learning Research (JMLR), saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. Instead, they wrote, they supported the model of JMLR, in which authors retained copyright over their papers and archives were freely available on the internet. Following the mass resignation, Kluwer changed their publishing policy to allow authors to self-archive their papers online after peer-review.

Abstracting and indexing The journal is abstracted and indexed in several databases, for example in:

Selected articles J.R. Quinlan (1986). "Induction of Decision Trees". Machine Learning. 1: 81–106. doi:10.1007/BF00116251. Nick Littlestone (1988). "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm" (PDF). Machine Learning. 2 (4): 285–318. doi:10.1007/BF00116827. Archived from the original (PDF) on 2022-07-02. Retrieved 2020-02-22.

John R. Anderson and Michael Matessa (1992). "Explorations of an Incremental, Bayesian Algorithm for Categorization". Machine Learning. 9 (4): 275–308. doi:10.1007/BF00994109. David Klahr (1994). "Children, Adults, and Machines as Discovery Systems". Machine Learning. 14 (3): 313–320. doi:10.1007/BF00993981. Thomas Dean and Dana Angluin and Kenneth Basye and Sean Engelson and Leslie Kaelbling and Evangelos Kokkevis and Oded Maron (1995). "Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning". Machine Learning. 18: 81–108. doi:10.1007/BF00993822. Luc De Raedt and Luc Dehaspe (1997). "Clausal Discovery". Machine Learning. 26 (2/3): 99–146. doi:10.1023/A:1007361123060. C. de la Higuera (1997). "Characteristic Sets for Grammatical Inference". Machine Learning. 27: 1–14. Robert E. Schapire and Yoram Singer (1999). "Improved Boosting Algorithms Using Confidence-rated Predictions". Machine Learning. 37 (3): 297–336. doi:10.1023/A:1007614523901. Robert E. Schapire and Yoram Singer (2000). "BoosTexter: A Boosting-based System for Text Categorization". Machine Learning. 39 (2/3): 135–168. doi:10.1023/A:1007649029923. P. Rossmanith and T. Zeugmann (2001). "Stochastic Finite Learning of the Pattern Languages". Machine Learning. 44 (1–2): 67–91. doi:10.1023/A:1010875913047. Parekh, Rajesh; Honavar, Vasant (2001). "Learning DFA from Simple Examples". Machine Learning. 44 (1/2): 9–35. doi:10.1023/A:1010822518073. Ayhan Demiriz and Kristin P. Bennett and John Shawe-Taylor (2002). "Linear Programming Boosting via Column Generation". Machine Learning. 46: 225–254. doi:10.1023/A:1012470815092. Simon Colton and Stephen Muggleton (2006). "Mathematical Applications of Inductive Logic Programming" (PDF). Machine Learning. 64 (1–3): 25–64. doi:10.1007/s10994-006-8259-x. Will Bridewell and Pat Langley and Ljupco Todorovski and Saso Dzeroski (2008). "Inductive Process Modeling". Machine Learning. Stephen Muggleton and Alireza Tamaddoni-Nezhad (2008). "QG/GA: a stochastic search for Progol". Machine Learning. 70 (2–3): 121–133. doi:10.1007/s10994-007-5029-3.

References

Worked examples

Example 1 — a first encounter with Machine Learning (journal)

Start with the simplest possible case. Write down what Machine Learning (journal) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, 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 Machine Learning (journal) 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 Machine Learning (journal) 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 Machine Learning (journal)

In research: Machine Learning (journal) appears in computer science 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 Machine Learning (journal) 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: Machine Learning (journal) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academic journals established in 1986, Computer science journal stubs, Computer science journals, so understanding it makes those chapters shorter.
In everyday life: Look for Machine Learning (journal) 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 Machine Learning (journal) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Machine Learning (journal) 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 Machine Learning (journal) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Machine Learning (journal) in simple terms?

Why does Machine Learning (journal) matter?

Because it connects several computer science 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 Machine Learning (journal)?

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 Machine Learning (journal).