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"description": "**Author**: Jock A. Blackard, Dr. Denis J. Dean, Dr. Charles W. Anderson\\n **Source**: Unknown\\n \\n **Note: This is a 19% subsample from the original UCI dataset (see version 4). The origin is currently not clear.**\\n \\n **Covertype**\\n Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types).\\n \\n This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices.\\n \\n Some background information for these four wilderness areas: Neota (area 2) probably has the highest mean elevational value of the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3) would have a lower mean elevational value, while Cache la Poudre (area 4) would have the lowest mean elevational value.\\n \\n As for primary major tree species in these areas, Neota would have spruce/fir (type 1), while Rawah and Comanche Peak would probably have lodgepole pine (type 2) as their primary species, followed by spruce/fir and aspen (type 5). Cache la Poudre would tend to have Ponderosa pine (type 3), Douglas-fir (type 6), and cottonwood/willow (type 4).\\n \\n The Rawah and Comanche Peak areas would tend to be more typical of the overall dataset than either the Neota or Cache la Poudre, due to their assortment of tree species and range of predictive variable values (elevation, etc.) Cache la Poudre would probably be more unique than the others, due to its relatively low elevation range and species composition.\\n \\n Attribute Information:\\n Given is the attribute name, attribute type, the measurement unit and a brief description. The forest cover type is the classification problem. The order of this listing corresponds to the order of numerals along the rows of the database.\\n >\\n Name / Data Type / Measurement / Description\\n Elevation / quantitative /meters / Elevation in meters\\n Aspect / quantitative / azimuth / Aspect in degrees azimuth\\n Slope / quantitative / degrees / Slope in degrees\\n Horizontal_Distance_To_Hydrology / quantitative / meters / Horz Dist to nearest surface water features\\n Vertical_Distance_To_Hydrology / quantitative / meters / Vert Dist to nearest surface water features\\n Horizontal_Distance_To_Roadways / quantitative / meters / Horz Dist to nearest roadway\\n Hillshade_9am / quantitative / 0 to 255 index / Hillshade index at 9am, summer solstice\\n Hillshade_Noon / quantitative / 0 to 255 index / Hillshade index at noon, summer solstice\\n Hillshade_3pm / quantitative / 0 to 255 index / Hillshade index at 3pm, summer solstice\\n Horizontal_Distance_To_Fire_Points / quantitative / meters / Horz Dist to nearest wildfire ignition points\\n Wilderness_Area (4 binary columns) / qualitative / 0 (absence) or 1 (presence) / Wilderness area designation\\n Soil_Type (40 binary columns) / qualitative / 0 (absence) or 1 (presence) / Soil Type designation\\n Cover_Type (7 types) / integer / 1 to 7 / Forest Cover Type designation\\n \\n \\n Relevant Papers:\\n - Blackard, Jock A. and Denis J. Dean. 2000. \"Comparative Accuracies of Artificial Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables.\" Computers and Electronics in Agriculture 24(3):131-151.\\n - Blackard, Jock A. and Denis J. Dean. 1998. \"Comparative Accuracies of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables.\" Second Southern Forestry GIS Conference. University of Georgia. Athens, GA. Pages 189-199.\\n - Blackard, Jock A. 1998. \"Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types.\" Ph.D. dissertation. Department of Forest Sciences. Colorado State University. Fort Collins, Colorado. 165 pages.",
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"description": "**Author**: Dominique Van Cappel, THOMSON-SINTRA \\n**Source**: [KEEL](http://sci2s.ugr.es/keel/dataset.php?cod=105#sub2), [ELENA](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/phoneme/) - 1993 \\n**Please cite**: None \\n\\nThe aim of this dataset is to distinguish between nasal (class 0) and oral sounds (class 1). Five different attributes were chosen to characterize each vowel: they are the amplitudes of the five first harmonics AHi, normalised by the total energy Ene (integrated on all the frequencies): AHi/Ene. The phonemes are transcribed as follows: sh as in she, dcl as in dark, iy as the vowel in she, aa as the vowel in dark, and ao as the first vowel in water. \\n\\n### Source\\n\\nThe current dataset was formatted by the KEEL repository, but originally hosted by the [ELENA Project](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/elena.htm#stuff). The dataset originates from the European ESPRIT 5516 project: ROARS. The aim of this project was the development and the implementation of a real time analytical system for French and Spanish speech recognition. \\n\\n### Relevant information\\n\\nMost of the already existing speech recognition systems are global systems (typically Hidden Markov Models and Time Delay Neural Networks) which recognizes signals and do not really use the speech\\nspecificities. On the contrary, analytical systems take into account the articulatory process leading to the different phonemes of a given language, the idea being to deduce the presence of each of the\\nphonetic features from the acoustic observation.\\n\\nThe main difficulty of analytical systems is to obtain acoustical parameters sufficiantly reliable. These acoustical measurements must :\\n\\n - contain all the information relative to the concerned phonetic feature.\\n - being speaker independent.\\n - being context independent.\\n - being more or less robust to noise.\\n\\nThe primary acoustical observation is always voluminous (spectrum x N different observation moments) and classification cannot been processed directly.\\n\\nIn ROARS, the initial database is provided by cochlear spectra, which may be seen as the output of a filters bank having a constant DeltaF/F0, where the central frequencies are distributed on a\\nlogarithmic scale (MEL type) to simulate the frequency answer of the auditory nerves. The filters outputs are taken every 2 or 8 msec (integration on 4 or 16 msec) depending on the type of phoneme\\nobserved (stationary or transitory). \\n\\nThe aim of the present database is to distinguish between nasal and\\noral vowels. There are thus two different classes:\\n\\n- Class 0 : Nasals \\n- Class 1 : Orals \\n\\nThis database contains vowels coming from 1809 isolated syllables (for example: pa, ta, pan,...). Five different attributes were chosen to characterize each vowel: they are the amplitudes of the five first harmonics AHi, normalised by the total energy Ene (integrated on all the frequencies): AHi/Ene. Each harmonic is signed: positive when it corresponds to a local maximum of the spectrum and negative otherwise.\\n\\nThree observation moments have been kept for each vowel to obtain 5427 different instances: \\n\\n - the observation corresponding to the maximum total energy Ene. \\n \\n - the observations taken 8 msec before and 8 msec after the observation corresponding to this maximum total energy.\\n\\nFrom these 5427 initial values, 23 instances for which the amplitude of the 5 first harmonics was zero were removed, leading to the 5404 instances of the present database. The patterns are presented in a random order.\\n\\n### Past Usage \\n\\nAlinat, P., Periodic Progress Report 4, ROARS Project ESPRIT II- Number 5516, February 1993, Thomson report TS. ASM 93/S/EGS/NC/079 \\n \\nGuerin-Dugue, A. and others, Deliverable R3-B4-P - Task B4: Benchmarks, Technical report, Elena-NervesII \"Enhanced Learning for Evolutive Neural Architecture\", ESPRIT-Basic Research Project Number 6891, June 1995 \\n\\nVerleysen, M. and Voz, J.L. and Thissen, P. and Legat, J.D., A statistical Neural Network for high-dimensional vector classification, ICNN'95 - IEEE International Conference on Neural Networks, November 1995, Perth, Western Australia. \\n \\nVoz J.L., Verleysen M., Thissen P. and Legat J.D., Suboptimal Bayesian classification by vector quantization with small clusters. ESANN95-European Symposium on Artificial Neural Networks, April 1995, M. Verleysen editor, D facto publications, Brussels, Belgium. \\n \\nVoz J.L., Verleysen M., Thissen P. and Legat J.D., A practical view of suboptimal Bayesian classification, IWANN95-Proceedings of the International Workshop on Artificial Neural Networks, June 1995, Mira, Cabestany, Prieto editors, Springer-Verlag Lecture Notes in Computer Sciences, Malaga, Spain",
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