Este documento explica los problemas aditivos y la suma o adición. Define la suma como la operación matemática de combinar o añadir dos números para obtener una cantidad final o total. También ilustra el proceso de juntar dos colecciones de objetos para obtener una sola colección. A continuación, presenta ejemplos de problemas aditivos que involucran la adición o cambio de cantidades y colecciones.
Java virtual machine - Notions de base :
Le langage Java,
Java Virtual Machine (JVM),
Introduction à la gestion de la mémoire Java,
Introduction au Garbage Collector,
The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7405343
Java virtual machine - Notions de base :
Le langage Java,
Java Virtual Machine (JVM),
Introduction à la gestion de la mémoire Java,
Introduction au Garbage Collector,
The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7405343
Ipsos, empresa de investigación de mercados y opinión pública, divulgó su informe N°29 “Claves Ipsos” correspondiente al mes de abril, que encuestó a 800 personas con el fin de identificar las principales opiniones y comportamientos de las y los ciudadanos respecto de temas de interés para el país. En esta edición se abordó la a Carabineros de Chile, su evaluación, legitimidad en su actuar y el asesinato de tres funcionarios en Cañete. Además, se consultó sobre el Ejército y la opinión respecto de la marcha en Putre.
1. Problemas aditivos
La sumao adiciónesla operación
matemáticade combinaro añadirdos
númerospara obtenerunacantidadfinal o
total.La sumatambiénilustrael procesode
juntardos coleccionesde objetosconel fin
de obtenerunasolacolección
AÑADIR O CAMBIO JUNTAR O CAMBIAR COMPARACIÓN IGUALACIÓN
Es el cual,las cantidades
incrementancuandose les
añade otra cantidada la
inicial ypor lomismoesta
cambia.
Es cuando dosdiferentes
cantidadesse juntanpara
hacer untodo.
Se utilizacuandose
quiere saberque
colecciónposee mas
partes.
Este se utilizacuandose
añade mas coleccionesala
existenteconlafinalidadde
poseerlamismacantidad.
Carlos tenía 6 galletas Y
Juan le regalo otras 2
galletas ¿Cuántas tiene
ahora?
¿Cuántosdulcesse comió
Claudia?Si se compró1
duvalin1 paletay1 chicle
De domingoaPaulasu
papá le dio$5.00Y su
mamá otros$5.00
¿Cuántodinerotiene?
Sara tenía 2 pulseras¿Cuántas
tiene ahora?Si su mamá le
compro 3 más.
REFEREENCIAS
SITIO WEB: http://es.slideshare.net/brenxhozt/5-problemas-aditivos
https://mx.answers.yahoo.com/question/index?qid=20100825171545AA
HP22t
SITIOWEB:http://mx.answers.yahoo.com/question/index?qid=200908031
