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Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A PLS kernel algorithm for data sets with many variables and few objects. Part II: Cross‐validation, missing data and examples
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Application of ALOGPS to predict 1‐octanol/water distribution coefficients, logP, and logD, of AstraZeneca in‐house database
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Learning to Learn
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Theoretical Models of Learning to Learn
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Lifelong Learning Algorithms
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The Nature of Statistical Learning Theory
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Virtual Computational Chemistry Laboratory – Design and Description
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Machine learning: An artificial intelligence approach
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Knowledge-based artificial neural networks
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MORE ROBUST CONCEPT LEARNING USING DYNAMICALLY – VARIABLE BIAS
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Multitask Learning: A Knowledge-Based Source of Inductive Bias
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QSAR modeling of blood:air and tissue:air partition coefficients using theoretical descriptors
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Calculation of lipophilicity for Pt(II) complexes: Experimental comparison of several methods
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Symbolic-neural systems and the use of hints for developing complex systems
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Efficient Partition of Learning Data Sets for Neural Network Training
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Neural network studies. 1. Comparison of overfitting and overtraining
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Prediction ofn-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices
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Neural Network Studies. 4. Introduction to Associative Neural Networks
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Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program
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Assessing Model Fit by Cross-Validation
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“In Silico” Design of New Uranyl Extractants Based on Phosphoryl-Containing Podands: QSPR Studies, Generation and Screening of Virtual Combinatorial Library, and Experimental Tests
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Collaborative Filtering on a Family of Biological Targets
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Benchmarking of Linear and Nonlinear Approaches for Quantitative Structure−Property Relationship Studies of Metal Complexation with Ionophores
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Application of Validated QSAR Models of D1Dopaminergic Antagonists for Database Mining
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Application of ALOGPS 2.1 to Predict log D Distribution Coefficient for Pfizer Proprietary Compounds
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A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling
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Multitask Learning
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Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement
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Associative Neural Network
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A tutorial on support vector regression
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Procedures for Detecting Outlying Observations in Samples
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Integration of Neural Heuristics into Knowledge-based Inference
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Nearest neighbor pattern classification
inferredcites →https://doi.org/10.1145/1015330.1015426openalex-snapshot
Multi-task feature and kernel selection for SVMs
inferredcites →https://doi.org/10.1186/1752-153x-2-s1-p13openalex-snapshot
Calculation of lipophilicity for Pt(II) complexes: experimental comparison of several methods
inferredcites →https://doi.org/10.1613/jair.731openalex-snapshot
A Model of Inductive Bias Learning
inferredcites →https://doi.org/10.21236/ada293563openalex-snapshot
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ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors
inferredcites →https://doi.org/10.3758/bf03198450openalex-snapshot
Knowledge-based classification of ill-defined categories
inferredcites →https://doi.org/10.4018/978-1-60566-010-3.ch002openalex-snapshot
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inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
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Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Additional file 1 of Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction
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