Zhou Zhihua Watermelon Book-AI English Words, Chapter One

1. • Data set: dataset
   • Example: instance
   • Attribute/feature: attribute, feature
   • Attribute space: attribute space
   • Sample space: sample space
   • Input space:
   • Feature vector: feature vector
   • 维数：dimensionality
   • Learning/training: learning training
   • Training data: training data
   • Training samples: training samples
   • Training set: training set
   • Hypothesis: hypothesis
   • Learner: learner
   • Prediction: prediction
   • Label: label
   • Example: example
   • Classification: classification (predicted discrete value)
   • Regression: regression (predict continuous values)
   • Two classification: binary classification, positive class: positive class, negative class: negative class
   • Multi-class classification: multi-class classification
   • The process of making predictions is called testing: testing
   • Test samples: testing samples
   • Clustering: clustering
   • Cluster: cluster
   • Learning tasks are divided into two categories: supervised learning (classification, regression) and unsupervised learning (clustering)
   • The ability of the learned model to apply to new samples is the ability of generalization
   • It is usually assumed that all samples in the sample space obey an unknown distribution (distribution)
   • Independent and indentically distributed
   • Induction (from special to general) and deduction (from general to special) are the two basic methods of scientific reasoning.
   • Learning from examples is an inductive process, called "induction learning"
   • There are two types of induction: broad sense and narrow sense
   • Version space: version space
   • Inductive bias: inductive bias
   • Occam's razor principle: occam's razor, if there are multiple hypotheses consistent with observations, choose the simplest one
   • Artificial Intelligence: artificial intelligence
   • Statistical learning: statistical learning
   • Support vector machine: support vector machine (kernel methods)
   • Crowdsourcing: crowdsourcing
   • Data mining: data mining