A-Z of Machine Learning and Computer Vision Terms

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A
A
AI Agent
AI Agent
AI Assistants
AI Assistants
AI Assisted Labeling
AI Assisted Labeling
Active Learning
Active Learning
Algorithm
Algorithm
Anchor Boxes
Anchor Boxes
Anomaly Detection
Anomaly Detection
Artificial Intelligence (AI)
Artificial Intelligence (AI)
Attribute
Attribute
B
B
Backpropagation
Backpropagation
Bagging
Bagging
Batch
Batch
Batch Normalization
Batch Normalization
Bayesian Network
Bayesian Network
Bias
Bias
Big Data
Big Data
Binary Classification
Binary Classification
Blur
Blur
Boosting
Boosting
Bounding Box
Bounding Box
C
C
COCO
COCO
Calibration
Calibration
Calibration Curve
Calibration Curve
Canonical Correlation Analysis (CCA)
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Case-Based Reasoning
Chain of Thought (CoT)
Chain of Thought (CoT)
ChatGPT
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary
Class Boundary
Class Boundary (Statistics & Machine Learning)
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Class Imbalance
Clustering
Clustering
Collaborative Filtering
Collaborative Filtering
Computer Vision
Computer Vision
Computer Vision Model
Computer Vision Model
Concept Drift
Concept Drift
Conditional Random Field (CRF)
Conditional Random Field (CRF)
Confusion Matrix
Confusion Matrix
Constrained Clustering
Constrained Clustering
Contrastive Learning
Contrastive Learning
Convolutional Neural Network (CNN)
Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)
Cross-Validation
Cross-Validation
D
D
DICOM
DICOM
Data Approximation
Data Approximation
Data Augmentation
Data Augmentation
Data Drift
Data Drift
Data Error
Data Error
Data Mining
Data Mining
Data Operations
Data Operations
Data Pre-processing
Data Pre-processing
Data Quality
Data Quality
Dataset
Dataset
Decision Boundary
Decision Boundary
Decision List
Decision List
Decision Stump
Decision Stump
Decision Tree
Decision Tree
Deep Learning
Deep Learning
Deep Neural Networks
Deep Neural Networks
Dimensionality Reduction
Dimensionality Reduction
Dropout
Dropout
Dynamic and Event-Based Classifications
Dynamic and Event-Based Classifications
E
E
Edge Cases
Edge Cases
Edge Computing
Edge Computing
Edge Detection
Edge Detection
Elastic Net
Elastic Net
Embedding Spaces
Embedding Spaces
Ensemble Learning
Ensemble Learning
Epoch
Epoch
Expectation-Maximization Algorithm (EM)
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
Extreme Learning Machine
F
F
F1 Score
F1 Score
FP-Growth Algorithm
FP-Growth Algorithm
Factor Analysis
Factor Analysis
False Positive Rate
False Positive Rate
Feature
Feature
Feature Engineering
Feature Engineering
Feature Extraction
Feature Extraction
Feature Hashing
Feature Hashing
Feature Learning
Feature Learning
Feature Scaling
Feature Scaling
Feature Selection
Feature Selection
Feature Vector
Feature Vector
Few-shot Learning
Few-shot Learning
Fisher’s Linear Discriminant
Fisher’s Linear Discriminant
Foundation Models
Foundation Models
Frame Rate
Frame Rate
Frames Per Second (FPS)
Frames Per Second (FPS)
Fully Connected Layer
Fully Connected Layer
Fuzzy Logic
Fuzzy Logic
G
G
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Generative Adversarial Networks
Generative Pre-Trained Transformer
Generative Pre-Trained Transformer
C

Case-Based Reasoning

Case-Based Reasoning (CBR) is an approach to problem-solving in artificial intelligence that involves reusing past experiences (cases) to solve new problems​.Instead of relying on general rules or an explicit model, a CBR system stores a knowledge base of cases, where each case is a specific problem situation paired with its solution (and often an explanation or outcome). When a new problem arises, the system retrieves a prior case that is similar to the current one and then reuses (adapts) that case’s solution to fit the new problem’s context​.This process is inspired by how humans often reason by analogy, recalling how a similar issue was resolved in the past and applying that experience to the current situation.In practice, case-based reasoning typically follows a structured four-step cycle​Retrieve: Identify the most similar past case(s) from the case library that resemble the new problem. (E.g., a help-desk system finds a past incident report that matches a new customer’s issue.)​Reuse: Copy or adapt the solution from the retrieved case to propose a solution for the current problem. Some adaptation may be needed if there are differences between the old and new cases​Revise: Test the proposed solution in the real world (or through simulation) and revise it if necessary. If the solution doesn’t fully solve the problem, adjust it until it works (this step is essentially error-correcting based on feedback)​Retain: After successfully solving the new problem, incorporate this experience as a new case into the case base for future reference​. The system thus “learns” by storing the solved case, enriching its knowledge for the next queries.CBR has been used in domains such as legal reasoning (where past legal cases inform decisions on new cases), customer support systems, and medical diagnosis. One advantage of CBR is its ability to provide explanations for solutions: since a solution is derived from a specific past case, the system can present that analogy (“We solved a similar issue this way before”). This approach naturally handles incremental learning (each new case solved becomes a training example for future problems) and can work even when an explicit general theory of the domain is hard to formulate. However, maintaining an efficient and relevant case library (avoiding case overload or redundancy) and designing good similarity metrics are important challenges in case-based reasoning systems.

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