ABSTRACT Estimates of reductions in greenhouse gas (GHG) emissions from lower demand for cattle‐based products must account for substitution effects. This study collected data through two surveys—one on ground beef and another on dairy milk—to evaluate substitution effects and potential GHG reductions.
Brandon R. McFadden +5 more
wiley +1 more source
Multivariate analysis for agro-morphological and quality traits in groundnut (Arachis hypogaea L.) genotypes in Eastern Ethiopia. [PDF]
Dama DB, Tesfamariam SA, Hassen AM.
europepmc +1 more source
Mineral Composition, Physicochemical Characteristics, and Antioxidant and Antibacterial Properties of Oil Extracted From Moroccan Bitter Apricot Kernels. [PDF]
El Hajjaji MA +4 more
europepmc +1 more source
Data-Driven Design Rules for Three-Dimensional Photonic Crystals. [PDF]
Cersonsky RK, Nayak SK, Lee SH.
europepmc +1 more source
Quantifying the spatial scales of animal clusters using density surfaces. [PDF]
van Mulken M +4 more
europepmc +1 more source
Application of Gaussian SVM Flame Detection Model Based on Color and Gradient Features in Engine Test Plume Images. [PDF]
Yan S, Gao Y, Zhang Z, Li Y.
europepmc +1 more source
Correcting Delocalization Error in Materials with Localized Orbitals and Linear-Response Screening. [PDF]
Williams JZ, Yang W.
europepmc +1 more source
Kernel Penalized K-means: A feature selection method based on Kernel K-means [PDF]
We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the cluster structure found with the initial feature set. It is based on the idea of simultaneously minimizing the violation of the initial cluster structure and penalizing the use of features via scaling factors. As the base method we
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exaly +5 more sources
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Simple multiple kernel k-means with kernel weight regularization
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SimpleMKKM: Simple Multiple Kernel K-Means
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix.
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