A-Z of Machine Learning and Computer Vision Terms
A
Activation Function
Active Learning
AI Agent
AI Assistants
AI Assisted Labeling
Algorithm
Anchor Boxes
Anomaly Detection
Artificial Intelligence (AI)
Attribute
B
Backpropagation
Backpropagation
Bagging
Batch
Batch Normalization
Bayesian Network
Bias
Big Data
Binary Classification
Blur
Boosting
Bounding Box
C
Calibration
Calibration Curve
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Chain of Thought (CoT)
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Clustering
COCO
Collaborative Filtering
Computer Vision
Computer Vision Model
Concept Drift
Conditional Random Field (CRF)
Confusion Matrix
Constrained Clustering
Contrastive Learning
Convolutional Neural Network (CNN)
Cross-entropy Loss
Cross-Validation
D
Data Approximation
Data Augmentation
Data Drift
Data Error
Data Mining
Data Operations
Data Pre-processing
Data Quality
Dataset
Decision Boundary
Decision List
Decision Stump
Decision Tree
Deep Learning
Deep Neural Networks
Deep Reinforcement Learning (DRL)
DICOM
Dimensionality Reduction
Dimensionality Reduction
Dropout
Dynamic and Event-Based Classifications
E
Edge Cases
Edge Computing
Edge Detection
Elastic Net
Embedding Spaces
Ensemble Learning
Epoch
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
F
F1 Score
Factor Analysis
False Positive Rate
Feature
Feature Extraction
Feature Hashing
Feature Learning
Feature Scaling
Feature Selection
Feature Vector
Fisher’s Linear Discriminant
Foundation Models
FP-Growth Algorithm
Frame Rate
Frames Per Second (FPS)
Fully Connected Layer
Fuzzy Logic
G
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Pattern Recognition
Pattern recognition is a field/problem of classifying data (observations, measurements) into categories or classes based on statistical information extracted from the patterns. It’s essentially the broader term that encompasses machine learning and some aspects of signal processing and AI dealing with recognition tasks (speech recognition, OCR, image recognition). Pattern recognition approaches can be supervised (training on labeled data) or unsupervised (clustering, discovering patterns). The “patterns” could be visual (like shapes in an image), auditory (sounds), or more abstract. The term is somewhat classical, used in context of early AI for things like fingerprint recognition, face recognition, etc. Many traditional pattern recognition techniques overlapped with what we now call machine learning (like Bayes classifiers, linear discriminants, decision trees). The field emphasizes feature extraction and the statistical nature of recognition. It gave rise to subfields like image processing, speech processing, etc., each focusing on patterns in specific data types.
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