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  2. 4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/cw/coursework3.pdf
    19 Nov 2023: You may use the barh command. For thatmultinomial model, what is the highest and lowest possible test set log probability (for anypossible test set)? ... c) For the Bayesian model, what is the log probability for the test document with ID 2001?Explain
  3. 4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/2122/cw/coursework3.pdf
    19 Nov 2023: c) For the Bayesian model, what is the log probability for the test document with ID 2001? ... Explainwhether, when computing the log probability of a test document, you would use the multinomial orthe categorical distribution function?
  4. nips.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/Ras96.pdf
    13 Feb 2023: Even locally around a mode the accuracy of the Gaussian approxi-mation is questionable, especially when the model is large compared to the amountof training data.Here I present and test ... Networks are trained on each of these partitions, and evaluated
  5. nips.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/WilRas96.pdf
    13 Feb 2023: There is one trainingcase (x(1); t(1)) and one test case for which we wish to predict y. ... The dotted line represents an observation y1 = t(1). In the right-hand plot we seethe distribution of the output for the test case, obtained by conditioning on
  6. SMEM Algorithm for Mixture Models

    https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha98a.pdf
    13 Feb 2023: operations to improve the likelihood of both the training data and of held-out test data. ... The data size was 200/class for training and 200/class for test.
  7. Modelling data

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/modelling%20data.pdf
    19 Nov 2023: generalize from observations in the training set to new test cases(interpolation and extrapolation). • ... make predictions on test cases• interpret the trained model, what insights is the model providing?• evaluate the accuracy of model. •
  8. Modelling data

    https://mlg.eng.cam.ac.uk/teaching/4f13/2122/modelling%20data.pdf
    19 Nov 2023: generalize from observations in the training set to new test cases(interpolation and extrapolation). • ... make predictions on test cases• interpret the trained model, what insights is the model providing?• evaluate the accuracy of model. •
  9. Predictive Automatic Relevance Determinationby Expectation…

    https://mlg.eng.cam.ac.uk/pub/pdf/QiMinPic04a.pdf
    13 Feb 2023: The first experiment has 30 random trainingpoints and 5000 random test points with dimension200. ... The estimated predictive performance is better correlated with the test errors thanevidence and sparsity.
  10. Communicated by David MacKay Pruning from Adaptive Regularization…

    https://mlg.eng.cam.ac.uk/pub/pdf/HanRas94.pdf
    13 Feb 2023: new test example for the specific student weight as estimated on the given training set. ... Complex Syst. 5, 603-643. Hansen, L. 1993. Stochastic linear learning: Exact test and training error aver- ages.
  11. 13 Feb 2023: However,it gives proper consideration to the uncertainty surrounding the test point and exactly computes themoments of the correct posterior distribution. ... 0.5log(x2(sin(2x)2)1). Figure 3: Comparison of models for suite of 6 test functions.

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