title: Text categorization using compression models

authors: Frank, Eibe

abstract: Text categorization, or the assignment of natural language texts to predefined categories based on their content, is of growing importance as the volume of information available on the internet continues to overwhelm us. The use of predefined categories implies a “supervised learning” approach to categorization, where already-classified articles—which effectively define the categories—are used as “training data” to build a model that can be used for classifying new articles that comprise the “test data.” This contrasts with “unsupervised” learning, where there is no training data and clusters of like documents are sought amongst the test articles. With supervised learning, meaningful labels (such as
keyphrases) are attached to the training documents, and appropriate labels can be assigned automatically to test documents depending on which category they fall into.

text: Text categorization using compression models Text categorization, or the assignment of natural language texts to predefined categories based on their content, is of growing importance as the volume of information available on the internet continues to overwhelm us. The use of predefined categories implies a “supervised learning” approach to categorization, where already-classified articles—which effectively define the categories—are used as “training data” to build a model that can be used for classifying new articles that comprise the “test data.” This contrasts with “unsupervised” learning, where there is no training data and clusters of like documents are sought amongst the test articles. With supervised learning, meaningful labels (such as
keyphrases) are attached to the training documents, and appropriate labels can be assigned automatically to test documents depending on which category they fall into.

year: 2000

title: Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers

authors: Born, Dennis‑Peter

subjects: Science & Technology

abstract: To provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare diferences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were analyzed. Two percentile datasets were established from individual swimmers’ annual best times and a two-way analysis of variance (ANOVA) was used to determine diferences between cross-sectional analysis and longitudinal tracking. A software-based percentile calculator was provided to extract the exact percentile for a given race time. Longitudinal tracking reduced the number of annual best times that were included in the percentiles by 98.35% to 262,071 and showed faster mean race times (P< 0.05) compared to the cross-sectional analysis. This diference was found in the lower percentiles (1st to 20th) across all age categories (P< 0.05); however, in the upper percentiles (80th to 99th), longitudinal tracking showed faster race times during early and late junior age only (P< 0.05), after which race times approximated cross-sectional tracking. The percentile calculator provides quick and easy data access to facilitate practical application of percentiles in training or competition. Longitudinal tracking that accounts for drop-out may predict performance progression towards elite age, particularly for high-performance swimmers.

text: Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers To provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare diferences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were analyzed. Two percentile datasets were established from individual swimmers’ annual best times and a two-way analysis of variance (ANOVA) was used to determine diferences between cross-sectional analysis and longitudinal tracking. A software-based percentile calculator was provided to extract the exact percentile for a given race time. Longitudinal tracking reduced the number of annual best times that were included in the percentiles by 98.35% to 262,071 and showed faster mean race times (P< 0.05) compared to the cross-sectional analysis. This diference was found in the lower percentiles (1st to 20th) across all age categories (P< 0.05); however, in the upper percentiles (80th to 99th), longitudinal tracking showed faster race times during early and late junior age only (P< 0.05), after which race times approximated cross-sectional tracking. The percentile calculator provides quick and easy data access to facilitate practical application of percentiles in training or competition. Longitudinal tracking that accounts for drop-out may predict performance progression towards elite age, particularly for high-performance swimmers.

year: 2022

title: Eke Panuku, Eke Tangaroa: He Taonga Tuku Iho

authors: Apanui, Ngahiwi

subjects: Panuku

abstract: He aha te Māori? He aha i kīia ai tētahi āhuatanga, tētahi mahi, tētahi tangata, tētahi whakahaere rānei he Māori? Ko te whāinga o te rangahau nei he rapa huarahi e āhei ai te whakautu i ēnei pātai. Me te huhua hoki o te whakaaro mō ēnei pātai, i huri ahau ki ngā mātanga, ngā pūkenga hoki o te ao mātauranga Māori hei taunaki i te whakautu ki te pātai. 
Ka tūhura te rangahau nei i te rētōtanga o te ao Māori, ka titiro hoki ki te āhua i Māori ai ētahi āhuatanga me te whakautu, he ahurea Māori i Māori ai tētahi āhuatanga, tētahi mahi, tētahi tangata, tētahi whakahaere rānei – ko te pū ahurea Māori tēnei. Ka titiro anō hoki te rangahau nei ki te whakaaro Māori hei tūāpapa mō te pū ahurea Māori, te wāhanga ki ngā kaupapa tuku iho me te whakaatu a te tikanga i te kaupapa. 
Ko te hua o te rangahau nei, he tuku i te rauemi e taea ai te tangata te tautuhi me te ine i te pū ahurea Māori hei ārahi i te aromātaitanga o ngā mahi Māori.

text: Eke Panuku, Eke Tangaroa: He Taonga Tuku Iho He aha te Māori? He aha i kīia ai tētahi āhuatanga, tētahi mahi, tētahi tangata, tētahi whakahaere rānei he Māori? Ko te whāinga o te rangahau nei he rapa huarahi e āhei ai te whakautu i ēnei pātai. Me te huhua hoki o te whakaaro mō ēnei pātai, i huri ahau ki ngā mātanga, ngā pūkenga hoki o te ao mātauranga Māori hei taunaki i te whakautu ki te pātai. 
Ka tūhura te rangahau nei i te rētōtanga o te ao Māori, ka titiro hoki ki te āhua i Māori ai ētahi āhuatanga me te whakautu, he ahurea Māori i Māori ai tētahi āhuatanga, tētahi mahi, tētahi tangata, tētahi whakahaere rānei – ko te pū ahurea Māori tēnei. Ka titiro anō hoki te rangahau nei ki te whakaaro Māori hei tūāpapa mō te pū ahurea Māori, te wāhanga ki ngā kaupapa tuku iho me te whakaatu a te tikanga i te kaupapa. 
Ko te hua o te rangahau nei, he tuku i te rauemi e taea ai te tangata te tautuhi me te ine i te pū ahurea Māori hei ārahi i te aromātaitanga o ngā mahi Māori.

year: 2015