Pattern Recognition and Machine Learning

Bishop, C. M., Recognition, P., & Machine Learning, S. (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

8 citations across 8 artifacts.

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

Primes

• Dimensionality Reduction

  • This sourceStandard supervised/unsupervised ML textbook.

• 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.

Domain-specific

• Hidden layer
  • … between constitutive identity and a convenient observable transfers from A canonical example satisfies the full defining rule of Hidden layer with all parameters and conventions explicit. to A careful use of Hidden layer tests the constitutive rule and nearest confusable rather than relying on topical resemblance..
• Underfitting
  • It also exposes a counterintuitive failure that the bare notion of "error" hides — that an intervention meant to help generalisation (regularisation) can, pushed too far, manufacture underfitting by starving the model of needed capacity

    This sourceThe curve-fitting demonstration that a regularization coefficient introduced to curb over-fitting yields a poor fit again when set too large.

    SupportedVerified against the source

    Read by an automated reader; not a human review. How support was checked

Mechanisms

• Model Complexity Penalty
  • Its strength is that parsimony becomes automatic and cheap: the penalty keeps models lean without spending human review bandwidth, and it is the textbook corrective for overfitting via regularization.

    This sourceExplains regularization as adding a penalty to the fitting objective to control overfitting.

Verification

Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.

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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

Registry ID ref:773f12488666 · see in the full table