850,732 research outputs found
Active learning for approximation of expensive functions with normal distributed output uncertainty
When approximating a black-box function, sampling with active learning
focussing on regions with non-linear responses tends to improve accuracy. We
present the FLOLA-Voronoi method introduced previously for deterministic
responses, and theoretically derive the impact of output uncertainty. The
algorithm automatically puts more emphasis on exploration to provide more
information to the models
Active Learning and Best-Response Dynamics
We examine an important setting for engineered systems in which low-power
distributed sensors are each making highly noisy measurements of some unknown
target function. A center wants to accurately learn this function by querying a
small number of sensors, which ordinarily would be impossible due to the high
noise rate. The question we address is whether local communication among
sensors, together with natural best-response dynamics in an
appropriately-defined game, can denoise the system without destroying the true
signal and allow the center to succeed from only a small number of active
queries. By using techniques from game theory and empirical processes, we prove
positive (and negative) results on the denoising power of several natural
dynamics. We then show experimentally that when combined with recent agnostic
active learning algorithms, this process can achieve low error from very few
queries, performing substantially better than active or passive learning
without these denoising dynamics as well as passive learning with denoising
Collaborative analysis of multi-gigapixel imaging data using Cytomine
Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries.
Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS
Many distributed machine learning frameworks have recently been built to
speed up the large-scale data learning process. However, most distributed
machine learning used in these frameworks still uses an offline algorithm model
which cannot cope with the data stream problems. In fact, large-scale data are
mostly generated by the non-stationary data stream where its pattern evolves
over time. To address this problem, we propose a novel Evolving Large-scale
Data Stream Analytics framework based on a Scalable Parsimonious Network based
on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving
algorithm is distributed over the worker nodes in the cloud to learn
large-scale data stream. Scalable PANFIS framework incorporates the active
learning (AL) strategy and two model fusion methods. The AL accelerates the
distributed learning process to generate an initial evolving large-scale data
stream model (initial model), whereas the two model fusion methods aggregate an
initial model to generate the final model. The final model represents the
update of current large-scale data knowledge which can be used to infer future
data. Extensive experiments on this framework are validated by measuring the
accuracy and running time of four combinations of Scalable PANFIS and other
Spark-based built in algorithms. The results indicate that Scalable PANFIS with
AL improves the training time to be almost two times faster than Scalable
PANFIS without AL. The results also show both rule merging and the voting
mechanisms yield similar accuracy in general among Scalable PANFIS algorithms
and they are generally better than Spark-based algorithms. In terms of running
time, the Scalable PANFIS training time outperforms all Spark-based algorithms
when classifying numerous benchmark datasets.Comment: 20 pages, 5 figure
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