Ethologia (Graece ἦθος 'natura, ingenium, proprietas' + -λογία 'studium') est scientificum et externum morum animalium studium, res zoologica, quae vehementius dicit mores animalium in condicionibus naturalibus,[1] contra behaviorismum?, qui vehementius dicit studia responsorum in laboratorio.
Index
1Index ethologorum
2Notae
3Bibliographia
4Nexus externi
Index ethologorum |
Ioannes-Baptista Lamarck (1744–1829)
Carolus Darwin (1809–1882)
Inter scientistas qui ethologiam magnopere adiuverunt (quorum multi hic perscripti revera sunt psychologi comparativi) sunt:
Robertus Ardrey
Patricius Bateson
Marcus Bekoff
Ingeborg Beling
Ioannes Bowlby
Donaldus Broom
Marianus Stamp Dawkins
Ricardus Dawkins
Vivtor Dolnik
Irenaeus Eibl-Eibesfeldt
Ioannes Endler
Ioannes-Henricus Fabre
Diana Fossey
Carolus von Frisch
Douglas P. Fry
Birutė Galdikas
Ioanna Goodall
Iacobus L. Gould
Iudita Hand
Clarentius Ellis Harbison
Heini Hediger
Anscharius Heinroth
Robertus Hinde
Bernardus Hollander
Sara Hrdy
Iulianus Huxley
Iulianus Jaynes
Ericus Klinghammer
Ioannes Krebs
Conradus Lorenz
Aubrey Manning
Eugenius Marais
Ioannes Pavlov
Patricia McConnell
Desmond Morris
Martinus Moynihan
Irena Pepperberg
Kevin Richardson
Georgius Romanes
Thomas Sebeok
Barbara Smuts
Gulielmus Homan Thorpe
Nicolaus Tinbergen
Iacobus von Uexküll
Fransiscus de Waal
Gulielmus Morton Wheeler
E. O. Wilson
Amotz Zahavi
Notae |
↑"Definition of ETHOLOGY". Merriam-Webster.
Bibliographia |
Burkhardt, Richard W. Jr. "On the Emergence of Ethology as a Scientific Discipline." Conspectus of History 1.7 (1981).
Klein, Z. 2000. The ethological approach to the study of human behavior. Neuroendocrinology Letters 21:477–481.
Shanor, Karen, et Jagmeet Kanwal. 2009. Bats Sing, Mice Giggle: Revealing the Secret Lives of Animals. Icon.
Shanor, Karen, et Jagmeet Kanwal. 2009. Accessible to the lay reader and acceptable to the scientific community. The Daily Telegraph, 10 Octobris.
Nexus externi |
Institutum Evolutionis et Investigationum Cognitivarum Conradi Lorenz, www.kli.ac.at
Situs Studii Integri Morum Animalium, www.indiana.edu
Ethologia Applicata, www.usask.ca
Societas Internationalis pro Ethologia Humana, ecolution.anthro.univie.ac.aet
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
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This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...