The inductive bias of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered (Mitchell, 1980).
inductive bias in a machine learning system, the effect of the data that are examined and the representation of learned information on the result of the learning process.
Converting Semantic Meta-Knowledge into Inductive Bias. In Proceedings of the 15th International Conference on Inductive Logic Programming, Bonn, Germany, August 2005.[4]
Schneider, Dave, C. Matuszek, P. Shah, R. Kahlert, D. Baxter, J. Cabral, M.
Quantifying the Inductive Bias in Concept Learning. This paper shows "that the notion of bias in inductive concept learning can be quantified in a way that directly relates to learning performance, ...
reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world.
learning to learn --- where the algorithm learns its own inductive bias based on previous experience.
4 Inductive Bias and Learnability 417
10.5 Knowledge and Learning 422
10.6 Unsupervised Learning 433
10.7 Reinforcement Learning 442
10.8 Epilogue and References 449
10.9 Exercises 450
11 MACHINE LEARNING: CONNECTIONIST 453
11.
Their ability to mix categorical and numerical data is another advantage. Inductive Bias: Shorter trees are preferred over larger ones.[5] Occam's Razor: Prefer the simplest hypothesis which fits the data. [6] ...
 See also: Machine learning, Neural network, Knowledge, Artificial intelligence, AI
  
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