5,352 research outputs found
Hyperparameter Importance Across Datasets
With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s).
Publication rights licensed to ACM. This is the author's version of the work.
It is posted here for your personal use, not for redistribution. The
definitive Version of Record was published in Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Minin
Is technical efficiency affected by farmersâ preference for mitigation and adaptation actions against climate change? A case study in northwest Mexico
Climate change has adverse effects on agriculture, decreasing crop quality and productivity. This makes it necessary to implement adaptation and mitigation strategies that contribute to the maintenance of technical efficiency (TE). This study analyzed the relationship of TE with farmersâ mitigation and adaptation action preferences, their risk and environmental attitudes, and their perception of climate change. Through the stochastic frontier method, TE levels were estimated for 370 farmers in Northwest Mexico. The results showed the average efficiency levels (57%) for three identified groups of farmers: High TE (15% of farmers), average TE (72%), and low TE (13%). Our results showed a relationship between two of the preferred adaptation actions against climate change estimated using the analytical hierarchy process (AHP) method. The most efficient farmers preferred âchange crops,â while less efficient farmers preferred âinvest in irrigation infrastructure.â The anthropocentric environmental attitude inferred from the New Ecological Paradigm (NEP) scale was related to the level of TE. Efficient farmers were those with an anthropocentric environmental attitude, compared to less efficient farmers, who exhibited an ecocentric attitude. The climate change issues were more perceived by moderately efficient farmers. These findings set out a roadmap for policy-makers to face climate change at the regional levelPeer ReviewedPostprint (published version
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2),
Special Issue on Accessing Information in Spoken Audio, September 200
EFFECTS OF RISK, DISEASE, AND NITROGEN SOURCE ON OPTIMAL NITROGEN FERTILIZATION RATES IN WINTER WHEAT PRODUCTION
Interactions among nitrogen (N) fertilization rate, N source, and disease severity can affect mean yield and yield variance in conservation tillage wheat production. A Just-Pope model was used to evaluate the effects of N rate, N source, and disease on the spring N-fertilization decision. Ammonium nitrate (AN) was the utility-maximizing N source regardless of risk preferences. The net-return-maximizing AN rate was 92 lb N/acre, providing 35.11/acre.Crop Production/Industries,
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