#classifiers

(1) a consonant harmony system I have not seen before but which makes excellent sense regardless:

(2) the perennial difficulties of assigning semantics to noun classifiers:

“Inanimate objects with a trunk” — this probably does not mean “statues of elephants”, so, an interesting way to express “trees”?

Hello and thanks for asking :D

So classifiers (also referred to as “counters”). 

First of all, the structure : 

When you count things in Korea you don’t say “one cat”, “two cows”, “three books”.

You would say something that can be translated as : cat, one, classifier-for-animals, have/exist. ex : 고양이가 한 마리 있어요.

Maybe you can see the idea as the following : in English, you often use words like any, some, a piece of, a slice of, a bunch of, ... There are always use when counting. Well it’s the same with classifiers.

There are a lot of different counters but here is a list for you to start (in bold the ones you probably need to learn first) :

명 - to count people

개 - to count objects (when in doubt, better use this one)

잔 - to count glasses, cups, etc (ex : a cup of coffee = 커피 한잔 주세요.)

병 - to count bottles

장 - to count sheets, or paper-like material in general (not to be confused with 잔 ^-^)

권 - to count books

마리 - to count animals

송이 - to count picked flowers

그루 -  to count trees

살 - when giving the age

대 - to count machines and pieces

벌 - to count clothes

곡 - to count pieces of music

편 - to count movies, dramas,poems, musicals 

Important :  When you use counters, most of the time, they are used along with native Korean numbers (하나. 둘. 셋)

To make a long story short : when counting things, say 

- what you are counting (ex : cat),

- how many/much (ex : 30, yeah that’s a lot of cats!! xD),

- which class of things the noun belongs to (ex : animals)

And don’t forget the question that goes with this lesson : Noun 몇 counter 있어요? (ex: 책이 몇 권 있어요?)

🧠 What Are Classifiers in Deep Learning, and How Do They Work? 🚀

Have you ever wondered how AI distinguishes between a cat and a dog, or spam and important emails? The answer lies in a fundamental concept of deep learning — Classifiers.

Classifiers are at the heart of artificial intelligence, enabling machines to categorize data, recognize patterns, and make intelligent decisions. From image recognition to fraud detection, classifiers shape how AI understands and interacts with the world.

💡 What You’ll Learn in This Video

✅ What Are Classifiers?

Understand how classifiers form the decision-making layer in deep learning systems. They analyze patterns and map input data to predefined categories — like “spam” or “not spam,” “disease” or “healthy.”

✅ How Classifiers Work

At their core, classifiers use decision boundaries, probability outputs, and activation functions to assign labels to data points.

Decision Boundaries: Define how data is separated into classes.

Probability Outputs: Estimate how confident the model is in its prediction.

Activation Functions: Help models learn non-linear relationships in data.

✅ Types of Classifiers

Explore the main types of classifiers that power modern AI systems:

Logistic Regression: For binary outcomes.

Support Vector Machines (SVM): For drawing clear classification lines.

Decision Trees: For hierarchical decision-making.

Neural Network-based Classifiers: For complex, high-dimensional data.

✅ Applications of Classifiers

Classifiers are used across every industry:

🩺 Healthcare: Disease prediction and diagnostics.

💳 Finance: Fraud detection and risk analysis.

💬 Marketing: Sentiment analysis and customer segmentation.

📸 Computer Vision: Image and facial recognition systems.

✅ Building Your Own Classifier

Start your journey with Python, TensorFlow, or Scikit-learn. These frameworks make it easy to train, test, and deploy custom classifiers using real-world datasets.

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This program gives you the expertise to build, train, and optimize classifiers — and the confidence to lead data-driven projects in the real world.

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🔖 Key Takeaways

Classifiers are the core of how AI categorizes data and makes predictions.

They use decision boundaries, probability scores, and activation functions to classify inputs.

Classifiers power AI use cases from spam detection to image recognition.

When I started writing about classifiers I teased that I'd give you a list of the ones I have so far. Now I finally had the time to compile it for you. :) Bear in mind that while it may appear longish, it certainly is not the entire list of classifiers Shembaba speakers have and I myself haven't found all of them out yet (= haven't created more yet).

I'll put them all in their nominative proximal determiner form ('this X') – giving all forms for each of them seems unreasonable to me. As a bonus I'll add some crude etymologies in case you need some inspiration for you classifier systems!

places: venia (< stain)

houses and buildings: vereiva (< building)

humanoids (at least elven-sized): vebe (< elf)

humanoids (child- or dwarf-sized): veshi (< part (in the hair) (because you can see the top of their head as a grown-up elf))

wild land animals: vespa (< flank)

wild water animals: veppa (< fin)

wild flying animals: vesān (< wing)

long and robust: vebō (< trunk)

long and flexible: velōr (< vine)

flat and flexible: velīn (< big leaf)

flat and robust, dishes: vekkaiv (< plate)

shapeless mass, hair: veieshi (< cloud)

edible plants: veker (< fruit)

poisonous plants, bad emotions: vepāl (< thorn)

domesticated animals, familiars, pets: venām (< friend, buddy)

insects, tiny animals, books: vekö (< insect, tiny scuttling animal)

words, songs and spells: vekāv (< song)

units of time: vesher (< sun)

round and spherical: vekiz (< pebble)

dangerous animals and monsters: velīm (< maw)

machines and contraptions: vemēv (< axis, yoke)

liquids in unspecified amounts (e.g. bodies of water): veshö (< wave, sloshing)

liquids in cups: vettup (< cup < cupped palms)

liquids in bottles: venarv (< bottle < 'carrier')

circles, ring-shapes, holes: veppōn (hole)

tools, weapons, instruments: venna (< tool < 'for-hands-thing')

stacks, ordered piles: verei (< stack)

heaps, unordered piles, dummy classifier for mixed groups and whatnots: vezāv (< pile)

vessels, containers: vekior (< vessel)

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