chore(wiki): dangling 링크 canonical 정규화 (768파일/1200건)

이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-08 12:24:15 +09:00
parent 2ddf30f8e4
commit d8a80f6272
768 changed files with 1085 additions and 1085 deletions
@@ -121,7 +121,7 @@ def adaptive_rag(query, user_profile):
 **기본값**: embedding retrieval + cross-encoder rerank + Thompson exploration.

 ## 🔗 Graph
- 부모: [[Recommender-Systems]] · [[Active-Learning]]
+- 부모: [[Recommender-Systems]] · [[Active Learning]]
 - 변형: [[Collaborative-Filtering]] · [[Multi-Armed-Bandit]]
 - 응용: [[RAG]]
 - Adjacent: [[Relevance-Feedback]] · [[Ranking-Algorithms]]
@@ -138,7 +138,7 @@ const Result = z.discriminatedUnion("tag", [
 **기본값**: discriminated union with explicit tag + exhaustive pattern match.

 ## 🔗 Graph
- 부모: [[Type-Theory]] · [[Algebraic-Data-Types]]
+- 부모: [[Type Theory]] · [[Algebraic-Data-Types]]
 - 응용: [[Error-Handling]] · [[State-Machine]]
 - Adjacent: [[Pattern-Matching]] · [[Curry-Howard]]

@@ -98,7 +98,7 @@ def stress_proxy(rmssd_ms: float, baseline: float) -> float:
 **기본값**: AUDIT-C → if positive: brief intervention + naltrexone trial + referral.

 ## 🔗 Graph
- 부모: [[Addiction-Neuroscience]]
+- 부모: [[Addiction Neuroscience]]
 - Adjacent: [[Dopamine]] · [[Cognitive-Behavioral-Therapy]]

 ## 🤖 LLM 활용
@@ -105,8 +105,8 @@ def coherence(events):

 ## 🔗 Graph
 - 변형: [[Memoir]] · [[Autoethnography]]
- 응용: [[Digital-Twin]]
- Adjacent: [[Working-Memory]]
+- 응용: [[Digital Twin]]
+- Adjacent: [[Working Memory]]

 ## 🤖 LLM 활용
 **언제**: personal assistant memory, retrospective query over journal, coaching reflection prompts.
@@ -123,7 +123,7 @@ checks = [
 - 부모: [[Ethnography]]
 - 변형: [[Memoir]]
 - 응용: [[AI-Ethics]]
- Adjacent: [[Autobiography]] · [[Grounded-Theory-Method]]
+- Adjacent: [[Autobiography]] · [[Grounded Theory Method]]

 ## 🤖 LLM 활용
 **언제**: studying one's own use of tech / LLM, insider perspective on team or community, theoretical reflection.
@@ -127,10 +127,10 @@ class ConformityRecord:
 **기본값**: HITL + audit log + bias monitoring + appeal channel.

 ## 🔗 Graph
- 부모: [[Decision-Theory]]
+- 부모: [[Decision Theory]]
 - 변형: [[HITL]]
 - 응용: [[Content-Moderation]]
- Adjacent: [[Algorithmic-Fairness]]
+- Adjacent: [[Algorithmic Fairness]]

 ## 🤖 LLM 활용
 **언제**: drafting decision policy, reviewing audit logs for anomalies, generating explanation text (Art.22), bias test fixture generation.
@@ -136,10 +136,10 @@ osm_xml = polygons_to_osm(polygons, tag={"building": "yes"})
 **기본값**: Perlin for terrain, WFC for tile-based, BSP for dungeons.

 ## 🔗 Graph
- 부모: [[Procedural-Content-Generation]] · [[Computational-Geometry]]
- 변형: [[Cellular-Automata]]
+- 부모: [[Procedural-Content-Generation]] · [[Computational Geometry (Frontend)]]
+- 변형: [[Cellular Automata]]
 - 응용: [[GIS]]
- Adjacent: [[Perlin-Noise]] · [[Diffusion-Models]] · [[Geographic-Information-Systems]]
+- Adjacent: [[Perlin Noise]] · [[Diffusion-Models]] · [[Geographic-Information-Systems]]

 ## 🤖 LLM 활용
 **언제**: parameter tuning suggestions, prompt-to-terrain via diffusion, level metric scoring (playability / aesthetic), debug seed reproduction.
@@ -120,7 +120,7 @@ def is_pareto(point, all_points):
 ## 🔗 Graph
 - 부모: [[Philosophy]]
 - 응용: [[AI_Safety_and_Alignment|AI-Alignment]]
- Adjacent: [[Aesthetic-Value]] · [[Decision-Theory]] · [[AI_Safety_and_Alignment|Constitutional-AI]]
+- Adjacent: [[Aesthetic-Value]] · [[Decision Theory]] · [[AI_Safety_and_Alignment|Constitutional-AI]]

 ## 🤖 LLM 활용
 **언제**: alignment policy drafting, principle articulation, value-laden decision review, ethical critique generation.
@@ -133,7 +133,7 @@ Suggest one tactic step. Output only the tactic.""")

 ## 🔗 Graph
 - 부모: [[Mathematical-Logic]]
- 변형: [[Type-Theory]]
+- 변형: [[Type Theory]]
 - 응용: [[Formal-Verification]] · [[Theorem-Proving]]
 - Adjacent: [[Godel-s-Incompleteness-Theorems]] · [[Curry-Howard]] · [[Theoretical-Computer-Science]]

@@ -131,9 +131,9 @@ def ising_step(spins, beta):
 **기본값**: weak emergence; demand operational definition + measurement.

 ## 🔗 Graph
- 부모: [[Complexity_Theory|Complexity-Theory]] · [[Systems-Theory]]
+- 부모: [[Complexity_Theory|Complexity-Theory]] · [[Systems Theory]]
 - 변형: [[Weak-Emergence]] · [[Strong-Emergence]] · [[Self-Organization]]
- 응용: [[Multi-agent-System|Multi-Agent-Systems]] · [[Cellular-Automata]] · [[LLM-Scaling]]
+- 응용: [[Multi-agent-System|Multi-Agent-Systems]] · [[Cellular Automata]] · [[LLM-Scaling]]
 - Adjacent: [[Global-Neuronal-Workspace]]

 ## 🤖 LLM 활용
@@ -180,8 +180,8 @@ print(differential_entropy(samples_narrow))  # negative
 **기본값**: 매 ML loss 의 cross-entropy. 매 distribution distance 의 KL (asymmetric). 매 symmetric 가 필요하면 JS divergence.

 ## 🔗 Graph
- 부모: [[Information_Theory|Information-Theory]] · [[Probability-Theory]]
- 응용: [[Cross-Entropy-Loss]] · [[KL-Divergence]] · [[Mutual-Information]] · [[Variational-Inference]]
+- 부모: [[Information_Theory|Information-Theory]] · [[Probability Theory]]
+- 응용: [[Cross-Entropy Loss]] · [[KL-Divergence]] · [[Mutual-Information]] · [[Variational-Inference]]

 ## 🤖 LLM 활용
 **언제**: 매 concept explanation, 매 derivation 의 walk-through, 매 ML loss function selection, 매 KL/cross-entropy 의 confused 시 disambiguation.
@@ -114,9 +114,9 @@ def expected_log_growth(p, weights, returns):
 **기본값**: assume non-ergodic in finance/biology/social; verify before using ensemble average.

 ## 🔗 Graph
- 부모: [[Probability-Theory]]
+- 부모: [[Probability Theory]]
 - 응용: [[MCMC]] · [[Reinforcement-Learning]]
- Adjacent: [[Markov-Chains]] · [[Entropy-in-Information-Theory]]
+- Adjacent: [[Markov-Chains]] · [[Entropy in Information Theory]]

 ## 🤖 LLM 활용
 **언제**: explain ergodicity intuitions, simulate ensemble vs time, derive Kelly fractions, debug MCMC non-convergence.
@@ -142,7 +142,7 @@ def redact_pii(s: str) -> str:

 ## 🔗 Graph
 - 부모: [[HCI]]
- 변형: [[Contextual-Inquiry]] · [[Auto-Ethnography]]
+- 변형: [[Contextual-Inquiry]] · [[Autoethnography]]
 - Adjacent: [[Grounded-Theory]]

 ## 🤖 LLM 활용
@@ -142,7 +142,7 @@ report cdc -severity error
 **기본값**: UVM for blocks + formal for control + emulation for system.

 ## 🔗 Graph
- 부모: [[Formal-Methods]]
+- 부모: [[Formal Methods]]
 - 변형: [[Formal-Verification]]
 - Adjacent: [[Model-Checking]] · [[Theorem-Proving]]

@@ -213,8 +213,8 @@ print(f"Match: {np.mean(recalled == patterns[0]):.2%}")

 ## 🔗 Graph
 - 부모: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Synaptic-Plasticity]]
- 응용: [[Hopfield-Network]] · [[Associative-Memory]] · [[Predictive-Coding]]
- Adjacent: [[Free-Energy-Principle]] · [[데이터_사이언스_및_ML_엔지니어링|Backpropagation]]
+- 응용: [[Hopfield Network]] · [[Associative-Memory]] · [[Predictive-Coding]]
+- Adjacent: [[Free-Energy-Principle]] · [[데이터 사이언스 및 ML 엔지니어링|Backpropagation]]

 ## 🤖 LLM 활용
 **언제**: 매 conceptual explanation, 매 history of computational neuroscience, 매 simple rule derivation.
@@ -117,7 +117,7 @@ q_hat = np.quantile(calib_residuals, 0.95)

 ## 🔗 Graph
 - 부모: [[Statistics]] · [[Probability Theory]]
- 변형: [[Bayesian-Inference]]
+- 변형: [[Bayesian Inference]]
 - 응용: [[Statistical-Power]] · [[Multivariate-Analysis]]
 - Adjacent: [[Epistemology]]

@@ -127,7 +127,7 @@ t, p = ttest_rel(ndcg_system_A, ndcg_system_B)
 **기본값**: NDCG@10, 매 paired-t test 매 statistical significance.

 ## 🔗 Graph
- 부모: [[Information-Retrieval]] · [[Statistics]]
+- 부모: [[Information Retrieval]] · [[Statistics]]
 - 변형: [[Ranking-Algorithms]] · [[Relevance-Feedback]]
 - 응용: [[Keyword Search]] · [[Knowledge Graph]]
 - Adjacent: [[Statistical-Power]]
@@ -124,7 +124,7 @@ h = differential_entropy(samples)  # ≈ 0.5*log(2πe) ≈ 1.42 nat
 - 부모: [[Entropy in Information Theory]] · [[Probability Theory]]
 - 변형: [[Mutual-Information]] · [[Kullback-Leibler-Divergence]] · [[Kolmogorov-Complexity]]
 - 응용: [[Entropy in Information Theory|Information Theory]] · [[Information Retrieval (IR)]]
- Adjacent: [[Statistical-Power]] · [[Bayesian-Inference]]
+- Adjacent: [[Statistical-Power]] · [[Bayesian Inference]]

 ## 🤖 LLM 활용
 **언제**: 매 loss design, 매 feature ranking, 매 sampling strategy 설명, 매 compression bound 추정.
@@ -145,7 +145,7 @@ xs_sm, Ps_sm = rts_smoother(xs, Ps, F, Q)  # 매 future info 활용
 - 부모: [[Probability Theory]] · [[Optimal-Control-Theory]] · [[Linear-Algebra-Foundations]]
 - 변형: [[Particle-Filter-Algorithms]]
 - 응용: [[Autonomous-Vehicle-Path-Planning]] · [[High-Frequency-Trading-Models]]
- Adjacent: [[Bayesian-Inference]] · [[Signal-Processing-Foundations]] · [[Gimbals-and-Orientation]]
+- Adjacent: [[Bayesian Inference]] · [[Signal-Processing-Foundations]] · [[Gimbals-and-Orientation]]

 ## 🤖 LLM 활용
 **언제**: 매 sensor fusion architecture 설계, 매 noise model tuning 가이드, 매 SLAM debugging.
@@ -149,9 +149,9 @@ class InvertedIndex:
 **기본값**: BM25 + RRF fusion with dense retriever.

 ## 🔗 Graph
- 부모: [[Information-Retrieval]]
+- 부모: [[Information Retrieval]]
 - 변형: [[BM25]] · [[TF-IDF]]
- 응용: [[Hybrid-Search]] · [[Semantic Search|Semantic-Search]]
+- 응용: [[Hybrid Search]] · [[Semantic Search|Semantic-Search]]
 - Adjacent: [[Tokenization]]

 ## 🤖 LLM 활용
@@ -138,7 +138,7 @@ def attention(Q, K, V, mask=None):

 ## 🔗 Graph
 - 변형: [[SVD]] · [[Eigendecomposition]]
- 응용: [[PCA]] · [[Attention-Mechanism]] · [[Linear-Regression]]
+- 응용: [[PCA]] · [[Attention Mechanism]] · [[Linear-Regression]]

 ## 🤖 LLM 활용
 **언제**: 매 ML 의 derivation, debugging matrix shapes, performance reasoning.
@@ -201,7 +201,7 @@ def is_balanced(node) -> bool:

 ## 🔗 Graph
 - 변형: [[Skip List]]
- 응용: [[AST Abstract Syntax Tree]] · [[DOM Tree]]
+- 응용: [[AST (Abstract Syntax Tree)]] · [[DOM Tree]]
 - Adjacent: [[Hash Functions and Maps]] · [[B-Tree]] · [[Trie]]

 ## 🤖 LLM 활용
@@ -113,8 +113,8 @@ def tree_to_tasks(node, owner=None):
 ## 🔗 Graph
 - 부모: [[Mental_Models|Mental Models]] · [[Mutually Exclusive and Collectively Exhaustive (MECE)]]
 - 변형: [[Issue Tree]] · [[Decision Theory]]
- 응용: [[Root-Cause-Analysis]] · [[5-Whys]]
- Adjacent: [[Pyramid-Principle]]
+- 응용: [[Root Cause Analysis]] · [[5-Whys]]
+- Adjacent: [[Pyramid Principle]]

 ## 🤖 LLM 활용
 **언제**: Open-ended problem decomposition, agent task planning, structured analysis.
@@ -113,7 +113,7 @@ def mc(key, n):
 ## 🔗 Graph
 - 부모: [[Statistics]]
 - 변형: [[MCMC]]
- 응용: [[Bayesian-Inference]] · [[RLHF]]
+- 응용: [[Bayesian Inference]] · [[RLHF]]

 ## 🤖 LLM 활용
 **언제**: High-dim integration, expectation under intractable distribution, simulation.
@@ -118,9 +118,9 @@ def mece_pareto(buckets, values, top=0.8):
 **기본값**: Quantitative MECE (numerical sum-check 가능).

 ## 🔗 Graph
- 부모: [[Mental_Models|Mental Models]] · [[Pyramid-Principle]]
+- 부모: [[Mental_Models|Mental Models]] · [[Pyramid Principle]]
 - 변형: [[Logic Trees]] · [[Issue Tree]]
- 응용: [[Root-Cause-Analysis]]
+- 응용: [[Root Cause Analysis]]
 - Adjacent: [[Decision Theory]]

 ## 🤖 LLM 활용
@@ -166,7 +166,7 @@ def align_classes(onto_a_labels, onto_b_labels, threshold=0.85):
 **기본값**: 매 enterprise → SKOS + RDFS; 매 reasoning critical → OWL 2 EL/QL profile.

 ## 🔗 Graph
- 부모: [[Knowledge Graph]] · [[Semantic Web]] · [[Knowledge Representation]]
+- 부모: [[Knowledge Graph]] · [[Semantic-Web]] · [[Knowledge Representation]]
 - 변형: [[OWL]]
 - 응용: [[GraphRAG]]

@@ -152,7 +152,7 @@ def value_iter(grid, f, l, dt, gamma=0.99, n_iter=500):
 ## 🔗 Graph
 - 부모: [[Control-Theory]] · [[Optimization]]
 - 응용: [[Robotics]] · [[Autonomous-Driving]]
- Adjacent: [[Reinforcement-Learning]] · [[Dynamic-Programming]] · [[데이터_사이언스_및_ML_엔지니어링|Bellman-Equation]]
+- Adjacent: [[Reinforcement-Learning]] · [[Dynamic-Programming]] · [[데이터 사이언스 및 ML 엔지니어링|Bellman-Equation]]

 ## 🤖 LLM 활용
 **언제**: cost-function design, MPC weight tuning rationale, Pontryagin/HJB derivation 매 explanation.
@@ -134,7 +134,7 @@ with m:

 ## 🔗 Graph
 - 부모: [[Probability Theory]] · [[Statistics]]
- 변형: [[MAP Estimation]]
+- 변형: [[MAP Estimation (Maximum A Posteriori)]]
 - 응용: [[Particle-Filter-Algorithms]] · [[Kalman-Filter-and-State-Tracking]]
 - Adjacent: [[Entropy in Information Theory|Information Theory]] · [[Decision Theory]] · [[Expectation-Maximization]]

@@ -120,7 +120,7 @@ def ips_loss(clicks, ranks, propensity):
 **기본값**: BM25 baseline + RM3 (k=10, w=0.5); dense는 HyDE for high-stakes RAG.

 ## 🔗 Graph
- 부모: [[Information-Retrieval]] · [[Query-Expansion]]
+- 부모: [[Information Retrieval]] · [[Query-Expansion]]
 - 변형: [[Pseudo-Relevance-Feedback]]
 - 응용: [[RAG]]
 - Adjacent: [[BM25]] · [[Dense-Retrieval]] · [[Reranking]] · [[ColBERT]]
@@ -165,7 +165,7 @@ def balanced_sampler(labels):
 ## 🔗 Graph
 - 부모: [[Statistics]] · [[Probability Theory|Probability-Theory-Foundations]]
 - 변형: [[Monte-Carlo-Integration]] · [[Particle-Filter-Algorithms]]
- 응용: [[데이터_사이언스_및_ML_엔지니어링|Reinforcement_Learning_Fundamentals]] · [[Information Retrieval (IR)]]
+- 응용: [[데이터 사이언스 및 ML 엔지니어링|Reinforcement_Learning_Fundamentals]] · [[Information Retrieval (IR)]]
 - Adjacent: [[Posterior-and-Prior-Probability]] · [[Statistical-Power]]

 ## 🤖 LLM 활용
@@ -165,7 +165,7 @@ fused = rrf([bm25_top, dense_top])
 **기본값**: normalized embeddings + cosine (= dot product on unit vectors).

 ## 🔗 Graph
- 부모: [[Information-Retrieval]] · [[Embeddings]]
+- 부모: [[Information Retrieval]] · [[Embeddings]]
 - 응용: [[RAG]] · [[Recommender-Systems]] · [[Clustering]]
 - Adjacent: [[FAISS]] · [[BM25]] · [[Locality-Sensitive-Hashing (LSH)|Locality-Sensitive-Hashing]]

@@ -154,7 +154,7 @@ def photometric_loss(I_pred, I_obs, mask=None):

 ## 🔗 Graph
 - 부모: [[3D-Reconstruction]]
- 변형: [[NeRF]] · [[3D-Gaussian-Splatting]]
+- 변형: [[NeRF]] · [[3D_Gaussian_Splatting]]
 - 응용: [[Mixed-Reality]]
 - Adjacent: [[SLAM]]

@@ -129,7 +129,7 @@ challenges = ["Executive summaries", "Big-picture strategy"]
 **기본값**: Treat as cognitive style (strengths + trade-offs), use multi-test battery.

 ## 🔗 Graph
- Adjacent: [[Theory-of-Mind]] · [[Working-Memory]]
+- Adjacent: [[Theory of Mind]] · [[Working Memory]]

 ## 🤖 LLM 활용
 **언제**: Modeling cognitive style differences. Designing assessment / UX for neurodivergent. Explaining detail-vs-gist trade-off in cognition.
@@ -161,10 +161,10 @@ Problem: {problem}"""
 **기본값**: Cowan's 4±1 chunks; chunk + offload to external memory (notes, scratchpad, retrieval).

 ## 🔗 Graph
- 부모: [[Cognitive-Psychology]]
+- 부모: [[Cognitive Psychology]]
 - 변형: [[Short-Term-Memory]]
 - 응용: [[Cognitive Load Theory|Cognitive-Load-Theory]] · [[Chunking]] · [[Attention]]
- Adjacent: [[Long-Term-Memory]] · [[Weak-Central-Coherence-Theory]]
+- Adjacent: [[Long-Term-Memory]] · [[Weak Central Coherence Theory]]

 ## 🤖 LLM 활용
 **언제**: Cognitive load reasoning, UX design, educational scaffolding, modeling LLM context use, ADHD/dementia assessment design.