This paper solves the open problem of exact learning geometric objects bounded by hyperplanes (and more generally by any constant degree algebraic surfaces) in the constant dimensional space from equivalence queries o...
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This paper solves the open problem of exact learning geometric objects bounded by hyperplanes (and more generally by any constant degree algebraic surfaces) in the constant dimensional space from equivalence queries only (i.e., in the on-line learning model). We present a novel approach that allows, under certain conditions, the composition of learning algorithms for simple classes into an algorithm for a more complicated class. Informally speaking, it shows that if a class of concepts C is learnable in time t using a small space then C*, the class of all functions of the form f (g1,..., gm) with g1,..., gm Ε C and any boolean function f, is learnable in polynomial time in t and m. We then show that the class of halfspaces in a fixed dimension space is learnable with a small space.
Federated learning (FL) provides an effective mechanism for distributed learning. However, it is expected to operate in a highly diverse setting with distinct behaviors from the participating nodes as well as dynamic ...
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We consider two broad families of non-additive loss functions covering a large number of applications: rational losses and tropical losses. We give new algorithms extending the Followthe- Perturbed-Leader (FPL) algori...
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Overcrowding in receiving patients, medical examinations, and treatment for hospital admission is common at most hospitals in Vietnam. Receiving and classifying patients is the first step in a medical facility’s medi...
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Traditional federated learning algorithms often face issues such as uneven data distribution and varying data quality, which can adversely affect model accuracy and convergence speed. To address these problems, we pro...
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The need to predict phage-bacteria interactions is a nowadays concern to overcome bacterial resistance issue;public genome databases contain highly imbalanced datasets which have hindered this task. Throughout this pa...
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Inductive algorithms rely strongly on their representational biases. Constructive induction can mitigate representational inadequacies. This paper introduces the notion of a relative gain measure and describes a new c...
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Inductive algorithms rely strongly on their representational biases. Constructive induction can mitigate representational inadequacies. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present the results which demonstrate the effectiveness of GALA on artificial and real domains for several learners: C4.5, CN2, perceptron and backpropagation.
Nowadays, pulmonary vascular disorders, which might result in pulmonary emboli or pulmonary hypertension, affect majority of patients. To diagnose alterations in vascular trees, a manual and automatic study of the ill...
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Purpose: We describe registration accuracy studies of a custom hardware-software system called eeDAP that registers fields of view (FOVs) of a glass slide on a microscope to the digital presentations of regions of int...
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The paper introduces the basic features of fuzzy neural logic network. Each fuzzy neural logic network model is trained from a set of knowledge in the form of examples using one of the three learning algorithms introd...
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