Example learning and evaluation trial under the NGM. by Spencer Caplan (10188380)

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All trial data from [16]. by Spencer Caplan (10188380)

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Major patterns to be captured by models of word learning and generalization. by Spencer Caplan (10188380)

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Implementation of distance computation between an object and mental representation under the NGM. by Spencer Caplan (10188380)

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Chart of all seven training configurations. by Spencer Caplan (10188380)

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NGM predictions and empirical human data across learning configurations with items presented sequentially. by Spencer Caplan (10188380)

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Second-block trial data from [16]. by Spencer Caplan (10188380)

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NGM predictions and empirical human data after training on a single item. by Spencer Caplan (10188380)

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Test-grid from Xu & Tanenbaum. by Spencer Caplan (10188380)

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Classical Predictive Coding for motion processing. by Elnaz Nemati (21402730)

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Code and test data for Polycenter by Ya-Ke Zhai (14248922)

Scripts for GC, CRC, and IBD in Python and R by Animesh Acharjee (6400427)

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Classification of PavCA Index scores with the k-Means algorithm where k = 3. by Camille Godin (21445879)

Positive predictive value (PPV, %) by survival time following first seizure code. by André Idegård (22041446)

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Implementation code and computational results.  by Xunqian Xu (21261899)

Flowchart for GR codes. by R. Sakthivel (2589547)

“…The algorithm was developed and coded in Verilog and simulated using Modelsim. …”

Learning achievement prediction results. by Tingting Tong (11993299)

“…This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. …”

Flowchart of the stacking method. by Tingting Tong (11993299)

“…This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. …”

Receiver operating characteristic curve. by Tingting Tong (11993299)

“…This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. …”

Related parameter settings of each model. by Tingting Tong (11993299)

“…This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. …”