docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-pattern-recognition
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title: Pattern Recognition
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Pattern Classification, Statistical Pattern Recognition]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [pattern-recognition, classification, ml, signal-processing]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: scikit-learn, pytorch
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---
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# Pattern Recognition
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## 매 한 줄
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> **"매 raw signal → category label"**. 매 1960s statistical pattern recognition (Bayes, kNN, LDA) → 1980s neural pattern recognition → 매 2020s deep learning 의 absorbed sub-field. 매 modern frame: 매 supervised classification + representation learning. Bishop's PRML (2006) 의 canonical reference.
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## 매 핵심
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### 매 history & framing
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- 매 1960s: Bayesian decision theory + linear classifiers.
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- 매 1970-80s: HMM (speech), template matching (OCR).
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- 매 1990s: SVM, kernel methods 의 dominance.
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- 매 2010s: DL absorbed it — "pattern recognition" 의 vintage term.
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- 매 modern: 매 ML/DL classification + representation learning.
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### 매 classical approaches
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- **Statistical**: Bayesian, MAP, ML, GMM, LDA, QDA.
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- **Neural**: perceptron → MLP → CNN → transformer.
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- **Structural / syntactic**: grammar-based, less common today.
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- **Template matching**: cross-correlation, used in OCR, fingerprint.
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- **Kernel methods**: SVM with RBF/poly kernels.
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### 매 pipeline (classic)
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1. Sensor / data acquisition.
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2. Preprocessing (denoise, normalize).
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3. Feature extraction (HOG, SIFT, MFCC) — 매 hand-crafted.
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4. Classifier (SVM, RF, NN).
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5. Post-processing (smoothing, thresholding).
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### 매 modern deep pipeline
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- 매 raw input → end-to-end DNN → label.
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- Feature extraction = learned (no HOG/SIFT).
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- Backbone (ResNet, ViT, CLIP) → head (linear / MLP).
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### 매 응용
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1. Computer vision (face, OCR, object detection).
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2. Speech recognition (Whisper, ASR).
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3. Biometrics (fingerprint, iris).
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4. Medical imaging (radiology AI).
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5. Anomaly detection (fraud, network intrusion).
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## 💻 패턴
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### Classic: Bayesian classifier
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```python
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from sklearn.naive_bayes import GaussianNB
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from sklearn.datasets import load_iris
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X, y = load_iris(return_X_y=True)
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clf = GaussianNB().fit(X, y)
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print(clf.score(X, y))
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```
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### Classic: SVM with RBF
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```python
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from sklearn.svm import SVC
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from sklearn.preprocessing import StandardScaler
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from sklearn.pipeline import make_pipeline
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clf = make_pipeline(StandardScaler(), SVC(kernel="rbf", C=1.0, gamma="scale"))
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clf.fit(X_train, y_train)
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```
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### Modern: CNN classification
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```python
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import torch.nn as nn
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import torchvision.models as tvm
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model = tvm.resnet50(weights=tvm.ResNet50_Weights.IMAGENET1K_V2)
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model.fc = nn.Linear(2048, num_classes) # transfer learn
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```
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### Modern: CLIP zero-shot
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```python
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import clip, torch
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model, preprocess = clip.load("ViT-B/32")
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classes = ["cat", "dog", "bird"]
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text = clip.tokenize([f"a photo of a {c}" for c in classes])
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with torch.no_grad():
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img_feat = model.encode_image(preprocess(image).unsqueeze(0))
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txt_feat = model.encode_text(text)
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logits = (img_feat @ txt_feat.T).softmax(-1)
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print(classes[logits.argmax()])
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```
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### HOG + SVM (classic CV pipeline)
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```python
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from skimage.feature import hog
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from sklearn.svm import LinearSVC
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features = [hog(img, pixels_per_cell=(8,8)) for img in images]
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clf = LinearSVC().fit(features, labels)
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```
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### Anomaly detection
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```python
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from sklearn.ensemble import IsolationForest
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detector = IsolationForest(contamination=0.01).fit(X_train)
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anomalies = detector.predict(X_test) == -1
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Tabular small data | RF / GBDT / SVM |
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| Image | Pretrained CNN/ViT (transfer) |
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| Speech / audio | Whisper / wav2vec finetune |
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| Few-shot / zero-shot | CLIP / SigLIP / VLM |
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| Anomaly (no labels) | IsolationForest / autoencoder |
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| Real-time embedded | Quantized CNN (MobileNet) |
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**기본값**: 매 image/speech 의 pretrained foundation model 의 fine-tune; tabular 의 GBDT.
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## 🔗 Graph
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- 부모: [[Machine-Learning]] · [[Statistical-Inference]]
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- 변형: [[Recognition]]
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- 응용: [[Computer Vision|Computer-Vision]] · [[OCR]] · [[Biometrics]]
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- Adjacent: [[Feature Engineering|Feature-Engineering]] · [[CLIP]]
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## 🤖 LLM 활용
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**언제**: 매 classification problem framing, choosing classical vs DL approach, transfer learning decision.
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**언제 X**: 매 generative tasks (use diffusion / LLM instead).
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## ❌ 안티패턴
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- **Hand-crafted features in 2026**: 매 HOG/SIFT 의 99% case 에서 pretrained CNN feature 가 우수.
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- **No baseline**: 매 jumping to DL without trying logistic / RF baseline.
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- **Class imbalance ignored**: 매 99% accuracy on 99/1 split = trivial. 매 use F1, ROC-AUC, balanced metrics.
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- **Test contamination**: 매 train/test split 의 leakage (especially time series).
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- **Calibration ignored**: 매 raw softmax ≠ probability. 매 use Platt scaling / temperature.
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## 🧪 검증 / 중복
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- Verified (Bishop "PRML" 2006, Duda & Hart "Pattern Classification" 2001).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — classical-to-modern framing, pipeline patterns |
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