2,368 research outputs found
Generalized Boosting Algorithms for Convex Optimization
Boosting is a popular way to derive powerful learners from simpler hypothesis
classes. Following previous work (Mason et al., 1999; Friedman, 2000) on
general boosting frameworks, we analyze gradient-based descent algorithms for
boosting with respect to any convex objective and introduce a new measure of
weak learner performance into this setting which generalizes existing work. We
present the weak to strong learning guarantees for the existing gradient
boosting work for strongly-smooth, strongly-convex objectives under this new
measure of performance, and also demonstrate that this work fails for
non-smooth objectives. To address this issue, we present new algorithms which
extend this boosting approach to arbitrary convex loss functions and give
corresponding weak to strong convergence results. In addition, we demonstrate
experimental results that support our analysis and demonstrate the need for the
new algorithms we present.Comment: Extended version of paper presented at the International Conference
on Machine Learning, 2011. 9 pages + appendix with proof
A Primal-Dual Convergence Analysis of Boosting
Boosting combines weak learners into a predictor with low empirical risk. Its
dual constructs a high entropy distribution upon which weak learners and
training labels are uncorrelated. This manuscript studies this primal-dual
relationship under a broad family of losses, including the exponential loss of
AdaBoost and the logistic loss, revealing:
- Weak learnability aids the whole loss family: for any {\epsilon}>0,
O(ln(1/{\epsilon})) iterations suffice to produce a predictor with empirical
risk {\epsilon}-close to the infimum;
- The circumstances granting the existence of an empirical risk minimizer may
be characterized in terms of the primal and dual problems, yielding a new proof
of the known rate O(ln(1/{\epsilon}));
- Arbitrary instances may be decomposed into the above two, granting rate
O(1/{\epsilon}), with a matching lower bound provided for the logistic loss.Comment: 40 pages, 8 figures; the NIPS 2011 submission "The Fast Convergence
of Boosting" is a brief presentation of the primary results; compared with
the JMLR version, this arXiv version has hyperref and some formatting tweak
GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes
Protein-protein interactions (PPIs) are critical for various biological
processes, and understanding their dynamics is essential for decoding molecular
mechanisms and advancing fields such as cancer research and drug discovery.
Mutations in PPIs can disrupt protein binding affinity and lead to functional
changes and disease. Predicting the impact of mutations on binding affinity is
valuable but experimentally challenging. Computational methods, including
physics-based and machine learning-based approaches, have been developed to
address this challenge. Machine learning-based methods, fueled by extensive PPI
datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in
predicting binding affinity changes. However, accurate predictions and
generalization of these models across different datasets remain challenging.
Geometric graph learning has emerged as a powerful approach, combining graph
theory and machine learning, to capture structural features of biomolecules. We
present GGL-PPI, a novel method that integrates geometric graph learning and
machine learning to predict mutation-induced binding free energy changes.
GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored
geometric subgraphs to extract informative features, demonstrating superior
performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and
SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased
predictions of GGL-PPI for both direct and reverse mutations. The findings
underscore the potential of GGL-PPI in accurately predicting binding free
energy changes, contributing to our understanding of PPIs and aiding drug
design efforts
Framework to Enhance Teaching and Learning in System Analysis and Unified Modelling Language
Cowling, MA ORCiD: 0000-0003-1444-1563; Munoz Carpio, JC ORCiD: 0000-0003-0251-5510Systems Analysis modelling is considered foundational for Information and Communication Technology (ICT) students, with introductory and advanced units included in nearly all ICT and computer science degrees. Yet despite this, novice systems analysts (learners) find modelling and systems thinking quite difficult to learn and master. This makes the process of teaching the fundamentals frustrating and time intensive. This paper will discuss the foundational problems that learners face when learning Systems Analysis modelling. Through a systematic literature review, a framework will be proposed based on the key problems that novice learners experience. In this proposed framework, a sequence of activities has been developed to facilitate understanding of the requirements, solutions and incremental modelling. An example is provided illustrating how the framework could be used to incorporate visualization and gaming elements into a Systems Analysis classroom; therefore, improving motivation and learning. Through this work, a greater understanding of the approach to teaching modelling within the computer science classroom will be provided, as well as a framework to guide future teaching activities
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