1. Applications and Problems
- Text or document classification, e.g., spam detection;
- Natural language processing, e.g., morphological analysis, Part-of-speech tagging, statistical parsing, named-entity Reco Gnition;
- SpeechRecognition, speech synthesis, speaker verification;
- Optical character recognition (OCR);
- Computational biology applications, e.g., protein function or structured prediction;
- Computer vision tasks, e.g., image recognition, face detection;
- Faud detection (credit card, telephone) and network intrusion;
- Games, e.g, chess, backgammon;
- Medical diagnosis;
- Recommendation Systems, search ENGINESM information extraction systems.
- dimensionality reduction or manifold learning
1.2 Definitions and terminology
- Training sample
- Validation sample
- Test sample
- Loss function
- Hypothesis Set
In practice, the amount of labeled data available are often too small to set aside a validation sample since that would Lea ve an insufficient amount of training data. Instead, a widely adopted method known as n-fold cross-validation is used to exploit the labeled Data bo Th for model selection (selection of the free parameters of the algorithm) and for training.
1.4 Learning Scenarios
- Supervised learning
- The learner receives a set of labeled examples as training data and makes predictions for all unseen points.
- Unsupervised learning
- the learner exclusively receives unlabeled training data, andmakes predictions for allunseen points.
- Semi-unsupervised Learning
- The learner receives a training sample consisting of both labeled and unlabeled data, and makes predictions for a ll unseen points.
- Transductive Inference
- As in the semi-supervised scenario, the learner receives a labeled training sample along with a set of unlabeled test Poin Ts. However, the objective of transductive inference is to predict labels only for these particular test points.
- On-line Learning
- In contrast with the previous scenarios, the online scenario
involves multiple rounds and training and testing phases are intermixed. At each
Round, the learner receives an unlabeled training point, makes a prediction, receives
The true label, and incurs a loss
- Reinforcement Learning
- the training and testing phases are ALS o intermixed in reinforcement learning. To collect information, the learner actively interacts with the environment and in some cases affects the environment, and receives an IMMEDIATE&NB SP; reward for Each action. The object of the learner is to maximize his reward over a course of actions and iterations with the Environment.
- Active Learning