Pattern Recognition and Machine Learning

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Type

Book

Intellectual base

Review or monograph

Year

2006

Link

https://link.springer.com/book/9780387310732

Cited from

mathematics, psychology, statistics_experimental_design

Cited by

5 citations across 5 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Dimensionality Reduction

  • This sourceStandard supervised/unsupervised ML textbook. Bibliography-only.

• Distributional Assumption
  • Removing these assumptions (e.g., fully nonparametric methods) buys flexibility but sacrifices computational efficiency.

    This sourceStandard graduate text on probabilistic machine learning: develops Gaussian (naive Bayes), mixture models, and latent-variable models in which an assumed distributional family for features or components enables closed-form posterior inference, EM, and variational methods — the ML instance of trading distributional flexibility for tractable inference.

• Learning
  • Learning encodes a structural pattern: experiential input → agent with modifiable internal state → update mechanism → durable capability change — a four-role decomposition that Sutton and Barto (2018) formalize in the reinforcement-learning setting (state, signal, update rule, retained policy) and that Bishop (2006) develops in the supervised-learning setting (parameters, training data, loss-driven update, learned model).

    This sourceStandard textbook treatment of supervised and unsupervised machine learning; develops parameter-update mechanisms (likelihood, loss, gradient methods) that instantiate the four-role learning pattern with silicon substrate, training data, differentiable update, and retained model weights.

• Overfitting

  • This sourceStandard textbook treatment of supervised and unsupervised machine learning; develops parameter-update mechanisms (likelihood, loss, gradient methods) that instantiate the four-role learning pattern with silicon substrate, training data, differentiable update, and retained model weights.

• Vector Space
  • Machine learning: feature vectors and embeddings where similarity is geometry; PCA, k-means, and neural-network layers all assume vector-space structure.

    This sourceFeature vectors and embeddings where similarity is geometry; PCA, k-means, and linear layers assume vector-space structure.

Verification

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/ ×1

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