Chapter 1-introduction

Source: Internet
Author: User

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.


    • Classification
    • Regression
    • Ranking
    • Clustering
    • dimensionality reduction or manifold learning

1.2 Definitions and terminology

    • Examples
    • Features
    • Labels
    • Training sample
    • Validation sample
    • Test sample
    • Loss function
    • Hypothesis Set

1.3 cross-validation

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
    • The learner adaptively or interactively collects training examples,
      Typically by querying a Oracle to request labels for new points.

Chapter 1-introduction

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