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You searched for subject:(koneoppiminen). Showing records 1 – 14 of 14 total matches.

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University of Helsinki

1. Hämäläinen, Mika. Sarkasmin automaattinen tunnistus – tämä toimii hyvin!.

Degree: Department of Modern Languages; Helsingfors universitet, Humanistiska fakulteten, Institutionen för moderna språk, 2016, University of Helsinki

 Tutkimuksen tavoitteena on yhtäältä tunnistaa sarkasmiin liittyviä piirteitä ja toisaalta luoda malli löydettyjen piirteiden pohjalta koneoppimisalgoritmia varten. Algoritmin tarkoituksena on tunnistaa sarkasmia automaattisesti. Sarkasmin piirteitä… (more)

Subjects/Keywords: sarkasmi; ohjattu koneoppiminen; sarkasmin tunnistus; Spansk filologi; Spanish Philology; espanjalainen filologia; sarkasmi; ohjattu koneoppiminen; sarkasmin tunnistus

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APA (6th Edition):

Hämäläinen, M. (2016). Sarkasmin automaattinen tunnistus – tämä toimii hyvin!. (Masters Thesis). University of Helsinki. Retrieved from http://hdl.handle.net/10138/163231

Chicago Manual of Style (16th Edition):

Hämäläinen, Mika. “Sarkasmin automaattinen tunnistus – tämä toimii hyvin!.” 2016. Masters Thesis, University of Helsinki. Accessed July 15, 2019. http://hdl.handle.net/10138/163231.

MLA Handbook (7th Edition):

Hämäläinen, Mika. “Sarkasmin automaattinen tunnistus – tämä toimii hyvin!.” 2016. Web. 15 Jul 2019.

Vancouver:

Hämäläinen M. Sarkasmin automaattinen tunnistus – tämä toimii hyvin!. [Internet] [Masters thesis]. University of Helsinki; 2016. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/10138/163231.

Council of Science Editors:

Hämäläinen M. Sarkasmin automaattinen tunnistus – tämä toimii hyvin!. [Masters Thesis]. University of Helsinki; 2016. Available from: http://hdl.handle.net/10138/163231


University of Oulu

2. Ferreira, E. (Eija). Model selection in time series machine learning applications.

Degree: 2015, University of Oulu

Abstract Model selection is a necessary step for any practical modeling task. Since the true model behind a real-world process cannot be known, the goal… (more)

Subjects/Keywords: machine learning; model selection; real-world applications; time series data; aikasarjadata; koneoppiminen; käytännön sovellukset; mallinvalinta

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APA (6th Edition):

Ferreira, E. (. (2015). Model selection in time series machine learning applications. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789526209012

Chicago Manual of Style (16th Edition):

Ferreira, E (Eija). “Model selection in time series machine learning applications.” 2015. Doctoral Dissertation, University of Oulu. Accessed July 15, 2019. http://urn.fi/urn:isbn:9789526209012.

MLA Handbook (7th Edition):

Ferreira, E (Eija). “Model selection in time series machine learning applications.” 2015. Web. 15 Jul 2019.

Vancouver:

Ferreira E(. Model selection in time series machine learning applications. [Internet] [Doctoral dissertation]. University of Oulu; 2015. [cited 2019 Jul 15]. Available from: http://urn.fi/urn:isbn:9789526209012.

Council of Science Editors:

Ferreira E(. Model selection in time series machine learning applications. [Doctoral Dissertation]. University of Oulu; 2015. Available from: http://urn.fi/urn:isbn:9789526209012

3. Similä, Timo. Advances in Variable Selection and Visualization Methods for Analysis of Multivariate Data.

Degree: 2007, Helsinki University of Technology

This thesis concerns the analysis of multivariate data. The amount of data that is obtained from various sources and stored in digital media is growing… (more)

Subjects/Keywords: machine learning; dimensionality reduction; regression; information visualization; variable selection; koneoppiminen; ulotteisuuden pienentäminen; regressio; informaation visualisointi; muuttujien valinta

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APA (6th Edition):

Similä, T. (2007). Advances in Variable Selection and Visualization Methods for Analysis of Multivariate Data. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2007/isbn9789512289301/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Similä, Timo. “Advances in Variable Selection and Visualization Methods for Analysis of Multivariate Data.” 2007. Thesis, Helsinki University of Technology. Accessed July 15, 2019. http://lib.tkk.fi/Diss/2007/isbn9789512289301/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Similä, Timo. “Advances in Variable Selection and Visualization Methods for Analysis of Multivariate Data.” 2007. Web. 15 Jul 2019.

Vancouver:

Similä T. Advances in Variable Selection and Visualization Methods for Analysis of Multivariate Data. [Internet] [Thesis]. Helsinki University of Technology; 2007. [cited 2019 Jul 15]. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512289301/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Similä T. Advances in Variable Selection and Visualization Methods for Analysis of Multivariate Data. [Thesis]. Helsinki University of Technology; 2007. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512289301/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Oulu

4. Väyrynen, E. (Eero). Emotion recognition from speech using prosodic features.

Degree: 2014, University of Oulu

Abstract Emotion recognition, a key step of affective computing, is the process of decoding an embedded emotional message from human communication signals, e.g. visual, audio,… (more)

Subjects/Keywords: affective computing; data visualisation; emotion recognition; machine learning; speech prosody; affektiivinen laskenta; emootiontunnistus; koneoppiminen; prosodiikka; tiedon visualisointi

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APA (6th Edition):

Väyrynen, E. (. (2014). Emotion recognition from speech using prosodic features. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789526204048

Chicago Manual of Style (16th Edition):

Väyrynen, E (Eero). “Emotion recognition from speech using prosodic features.” 2014. Doctoral Dissertation, University of Oulu. Accessed July 15, 2019. http://urn.fi/urn:isbn:9789526204048.

MLA Handbook (7th Edition):

Väyrynen, E (Eero). “Emotion recognition from speech using prosodic features.” 2014. Web. 15 Jul 2019.

Vancouver:

Väyrynen E(. Emotion recognition from speech using prosodic features. [Internet] [Doctoral dissertation]. University of Oulu; 2014. [cited 2019 Jul 15]. Available from: http://urn.fi/urn:isbn:9789526204048.

Council of Science Editors:

Väyrynen E(. Emotion recognition from speech using prosodic features. [Doctoral Dissertation]. University of Oulu; 2014. Available from: http://urn.fi/urn:isbn:9789526204048


University of Oulu

5. Huang, X. (Xiaohua). Methods for facial expression recognition with applications in challenging situations.

Degree: 2014, University of Oulu

Abstract In recent years, facial expression recognition has become a useful scheme for computers to affectively understand the emotional state of human beings. Facial representation… (more)

Subjects/Keywords: computer vision; facial expression recognition; feature extraction; local binary pattern; machine learning; LBP-menetelmä; kasvonilmeiden tunnistaminen; konenäkö; koneoppiminen; piirteiden ilmaisu

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APA (6th Edition):

Huang, X. (. (2014). Methods for facial expression recognition with applications in challenging situations. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789526206561

Chicago Manual of Style (16th Edition):

Huang, X (Xiaohua). “Methods for facial expression recognition with applications in challenging situations.” 2014. Doctoral Dissertation, University of Oulu. Accessed July 15, 2019. http://urn.fi/urn:isbn:9789526206561.

MLA Handbook (7th Edition):

Huang, X (Xiaohua). “Methods for facial expression recognition with applications in challenging situations.” 2014. Web. 15 Jul 2019.

Vancouver:

Huang X(. Methods for facial expression recognition with applications in challenging situations. [Internet] [Doctoral dissertation]. University of Oulu; 2014. [cited 2019 Jul 15]. Available from: http://urn.fi/urn:isbn:9789526206561.

Council of Science Editors:

Huang X(. Methods for facial expression recognition with applications in challenging situations. [Doctoral Dissertation]. University of Oulu; 2014. Available from: http://urn.fi/urn:isbn:9789526206561


Tampere University

6. Saarikoski, Jyri. On text document classification and retrieval using self-organising maps .

Degree: 2014, Tampere University

 Tekstidokumenttien automaattista luokittelua ja tiedonhakua itseorganisoituvilla kartoilla Tutkimus käsittelee sähköisessä muodossa olevien tekstidokumenttien automaattista luokittelua ja tiedonhakua. Tekstidokumenttien automaattisessa luokittelussa tavoitteena on kehittää tietokoneohjelma, joka… (more)

Subjects/Keywords: Tiedonhaku; tekstien luokittelu; tiedonlouhinta; koneoppiminen; itseorganisoituvat kartat; Information retrieval; Text Classification; Data mining; Machine learning; Self-organising maps

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APA (6th Edition):

Saarikoski, J. (2014). On text document classification and retrieval using self-organising maps . (Doctoral Dissertation). Tampere University. Retrieved from http://tampub.uta.fi/handle/10024/96251

Chicago Manual of Style (16th Edition):

Saarikoski, Jyri. “On text document classification and retrieval using self-organising maps .” 2014. Doctoral Dissertation, Tampere University. Accessed July 15, 2019. http://tampub.uta.fi/handle/10024/96251.

MLA Handbook (7th Edition):

Saarikoski, Jyri. “On text document classification and retrieval using self-organising maps .” 2014. Web. 15 Jul 2019.

Vancouver:

Saarikoski J. On text document classification and retrieval using self-organising maps . [Internet] [Doctoral dissertation]. Tampere University; 2014. [cited 2019 Jul 15]. Available from: http://tampub.uta.fi/handle/10024/96251.

Council of Science Editors:

Saarikoski J. On text document classification and retrieval using self-organising maps . [Doctoral Dissertation]. Tampere University; 2014. Available from: http://tampub.uta.fi/handle/10024/96251


University of Helsinki

7. Poutanen, Julia. Classification automatique des SMS – analyse des caractéristiques langagières de deux groupes d’âge.

Degree: Department of Modern Languages; Helsingfors universitet, Humanistiska fakulteten, Institutionen för moderna språk, 2018, University of Helsinki

 Tämän tutkielman tarkoituksena on tutkia aikuisten ja nuorten kielenkäytön välisiä eroja ranskankielisessä tekstiviestiaineistossa. Eroja tutkitaan luomalla koneoppimista hyödyntävä automaattinen luokittelija, joka kykenee erottelemaan aikuisten ja… (more)

Subjects/Keywords: tekstiviestit; koneoppiminen; ikä; luokittelu; ranskan kieli; kieliteknologia; SMS; apprentissage automatique; âge; langue française; linguistique informatique; ranskalainen filologia; French Philology; Fransk filologi; tekstiviestit; koneoppiminen; ikä; luokittelu; ranskan kieli; kieliteknologia; SMS; apprentissage automatique; âge; langue française; linguistique informatique

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APA (6th Edition):

Poutanen, J. (2018). Classification automatique des SMS – analyse des caractéristiques langagières de deux groupes d’âge. (Masters Thesis). University of Helsinki. Retrieved from http://hdl.handle.net/10138/236112

Chicago Manual of Style (16th Edition):

Poutanen, Julia. “Classification automatique des SMS – analyse des caractéristiques langagières de deux groupes d’âge.” 2018. Masters Thesis, University of Helsinki. Accessed July 15, 2019. http://hdl.handle.net/10138/236112.

MLA Handbook (7th Edition):

Poutanen, Julia. “Classification automatique des SMS – analyse des caractéristiques langagières de deux groupes d’âge.” 2018. Web. 15 Jul 2019.

Vancouver:

Poutanen J. Classification automatique des SMS – analyse des caractéristiques langagières de deux groupes d’âge. [Internet] [Masters thesis]. University of Helsinki; 2018. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/10138/236112.

Council of Science Editors:

Poutanen J. Classification automatique des SMS – analyse des caractéristiques langagières de deux groupes d’âge. [Masters Thesis]. University of Helsinki; 2018. Available from: http://hdl.handle.net/10138/236112


University of Oulu

8. Mantere, M. (Matti). Network security monitoring and anomaly detection in industrial control system networks.

Degree: 2015, University of Oulu

Abstract Industrial control system (ICS) networks used to be isolated environments, typically separated by physical air gaps from the wider area networks. This situation has… (more)

Subjects/Keywords: anomaly detection; cybersecurity; industrial control system security; information security; intrusion detection; machine learning; network security; automaatiojärjestelmien turvallisuus; koneoppiminen; kyberturvallisuus; poikkeamien havainnointi; tietoturva; tunkeutumisen havainnointi

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APA (6th Edition):

Mantere, M. (. (2015). Network security monitoring and anomaly detection in industrial control system networks. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789526208152

Chicago Manual of Style (16th Edition):

Mantere, M (Matti). “Network security monitoring and anomaly detection in industrial control system networks.” 2015. Doctoral Dissertation, University of Oulu. Accessed July 15, 2019. http://urn.fi/urn:isbn:9789526208152.

MLA Handbook (7th Edition):

Mantere, M (Matti). “Network security monitoring and anomaly detection in industrial control system networks.” 2015. Web. 15 Jul 2019.

Vancouver:

Mantere M(. Network security monitoring and anomaly detection in industrial control system networks. [Internet] [Doctoral dissertation]. University of Oulu; 2015. [cited 2019 Jul 15]. Available from: http://urn.fi/urn:isbn:9789526208152.

Council of Science Editors:

Mantere M(. Network security monitoring and anomaly detection in industrial control system networks. [Doctoral Dissertation]. University of Oulu; 2015. Available from: http://urn.fi/urn:isbn:9789526208152


University of Oulu

9. Suutala, J. (Jaakko). Learning discriminative models from structured multi-sensor data for human context recognition.

Degree: 2012, University of Oulu

Abstract In this work, statistical machine learning and pattern recognition methods were developed and applied to sensor-based human context recognition. More precisely, we concentrated on… (more)

Subjects/Keywords: Bayesian filtering; activity recognition; biometrics; context-awareness; kernel methods; machine learning; pattern recognition; person tracking; Bayesiläinen suodatus; aktiviteetin tunnistus; biometrinen tunnistus; hahmontunnistus; henkilön seuranta; koneoppiminen; tilannetietoisuus; ydinmenetelmät

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APA (6th Edition):

Suutala, J. (. (2012). Learning discriminative models from structured multi-sensor data for human context recognition. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789514298493

Chicago Manual of Style (16th Edition):

Suutala, J (Jaakko). “Learning discriminative models from structured multi-sensor data for human context recognition.” 2012. Doctoral Dissertation, University of Oulu. Accessed July 15, 2019. http://urn.fi/urn:isbn:9789514298493.

MLA Handbook (7th Edition):

Suutala, J (Jaakko). “Learning discriminative models from structured multi-sensor data for human context recognition.” 2012. Web. 15 Jul 2019.

Vancouver:

Suutala J(. Learning discriminative models from structured multi-sensor data for human context recognition. [Internet] [Doctoral dissertation]. University of Oulu; 2012. [cited 2019 Jul 15]. Available from: http://urn.fi/urn:isbn:9789514298493.

Council of Science Editors:

Suutala J(. Learning discriminative models from structured multi-sensor data for human context recognition. [Doctoral Dissertation]. University of Oulu; 2012. Available from: http://urn.fi/urn:isbn:9789514298493


Tampere University

10. Siermala, Markku. Local prediction of secondary structures of proteins from viewpoints of rare structure .

Degree: fi=Tietojenkäsittelytieteiden laitos | en=Department of Computer Sciences|, 2002, Tampere University

 Proteiinit eli valkuaisaineet ovat elämän ja solun toiminnan kannalta keskeisiä makromolekyylejä. Valkuaisaineiden rakennetutkimus on tärkeätä, kun selvitetään proteiinien rakennetta ja toimintaa geneettisen informaation ja valikuaisaineiden… (more)

Subjects/Keywords: Proteiinien sekundaarirakenneennustus; neuroverkot; koneoppiminen; secondary structure prediction; neural network; machine learning

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APA (6th Edition):

Siermala, M. (2002). Local prediction of secondary structures of proteins from viewpoints of rare structure . (Doctoral Dissertation). Tampere University. Retrieved from http://tampub.uta.fi/handle/10024/67198

Chicago Manual of Style (16th Edition):

Siermala, Markku. “Local prediction of secondary structures of proteins from viewpoints of rare structure .” 2002. Doctoral Dissertation, Tampere University. Accessed July 15, 2019. http://tampub.uta.fi/handle/10024/67198.

MLA Handbook (7th Edition):

Siermala, Markku. “Local prediction of secondary structures of proteins from viewpoints of rare structure .” 2002. Web. 15 Jul 2019.

Vancouver:

Siermala M. Local prediction of secondary structures of proteins from viewpoints of rare structure . [Internet] [Doctoral dissertation]. Tampere University; 2002. [cited 2019 Jul 15]. Available from: http://tampub.uta.fi/handle/10024/67198.

Council of Science Editors:

Siermala M. Local prediction of secondary structures of proteins from viewpoints of rare structure . [Doctoral Dissertation]. Tampere University; 2002. Available from: http://tampub.uta.fi/handle/10024/67198

11. Raiko, Tapani. Bayesian Inference in Nonlinear and Relational Latent Variable Models.

Degree: 2006, Helsinki University of Technology

Statistical data analysis is becoming more and more important when growing amounts of data are collected in various fields of life. Automated learning algorithms provide… (more)

Subjects/Keywords: machine learning; graphical models; probabilistic reasoning; nonlinear models; variational methods; state-space models; hidden Markov models; inductive logic programming; first-order logic; koneoppiminen; graafiset mallit; todennäköisyyslaskentaan perustuva päättely; epälineaariset mallit; variaatiomenetelmät; tila-avaruusmallit; piilo-Markov -malli; induktiivinen logiikkaohjelmointi; ensimmäisen kertaluvun logiikka

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APA (6th Edition):

Raiko, T. (2006). Bayesian Inference in Nonlinear and Relational Latent Variable Models. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2006/isbn951228510X/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Raiko, Tapani. “Bayesian Inference in Nonlinear and Relational Latent Variable Models.” 2006. Thesis, Helsinki University of Technology. Accessed July 15, 2019. http://lib.tkk.fi/Diss/2006/isbn951228510X/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Raiko, Tapani. “Bayesian Inference in Nonlinear and Relational Latent Variable Models.” 2006. Web. 15 Jul 2019.

Vancouver:

Raiko T. Bayesian Inference in Nonlinear and Relational Latent Variable Models. [Internet] [Thesis]. Helsinki University of Technology; 2006. [cited 2019 Jul 15]. Available from: http://lib.tkk.fi/Diss/2006/isbn951228510X/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raiko T. Bayesian Inference in Nonlinear and Relational Latent Variable Models. [Thesis]. Helsinki University of Technology; 2006. Available from: http://lib.tkk.fi/Diss/2006/isbn951228510X/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Tampere University

12. Järvelin, Antti. Applying Machine Learning Methods to Aphasic Data .

Degree: fi=Tietojenkäsittelytieteiden laitos | en=Department of Computer Sciences|, 2008, Tampere University

 Afasia on aivoperäinen puheen tuottamis- ja ymmärtämishäiriö, jonka seurauksena potilas on menettänyt joko kokonaan tai osittain kykynsä lukea, kirjoittaa, puhua tai ymmärtää puhuttua kieltä. Afasiapotilaiden… (more)

Subjects/Keywords: koneoppiminen; neuroverkot; luokittelu; monikerroksinen perceptron; afasia; machine learning; neural networks; classification; multi-layer perceptron; aphasia

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APA (6th Edition):

Järvelin, A. (2008). Applying Machine Learning Methods to Aphasic Data . (Doctoral Dissertation). Tampere University. Retrieved from http://tampub.uta.fi/handle/10024/67865

Chicago Manual of Style (16th Edition):

Järvelin, Antti. “Applying Machine Learning Methods to Aphasic Data .” 2008. Doctoral Dissertation, Tampere University. Accessed July 15, 2019. http://tampub.uta.fi/handle/10024/67865.

MLA Handbook (7th Edition):

Järvelin, Antti. “Applying Machine Learning Methods to Aphasic Data .” 2008. Web. 15 Jul 2019.

Vancouver:

Järvelin A. Applying Machine Learning Methods to Aphasic Data . [Internet] [Doctoral dissertation]. Tampere University; 2008. [cited 2019 Jul 15]. Available from: http://tampub.uta.fi/handle/10024/67865.

Council of Science Editors:

Järvelin A. Applying Machine Learning Methods to Aphasic Data . [Doctoral Dissertation]. Tampere University; 2008. Available from: http://tampub.uta.fi/handle/10024/67865


Tampere University

13. Laurikkala, Jorma. Knowledge Discovery for Female Urinary Incontinence Expert System .

Degree: fi=Tietojenkäsittelytieteiden laitos | en=Department of Computer Sciences|, 2001, Tampere University

 Asiantuntijajärjestelmät ovat tarkasti ongelma-alueelle rajattuja tietokoneohjelmia, joiden 'älykäs' ihmisasiantuntijan päättelyä jäljittelevä toiminta perustuu ongelmaa koskevaan tietämykseen. Tietämyksen voidaan ajatella olevan informaatiota, jonka asiantuntija on oppinut… (more)

Subjects/Keywords: ekoäly; asiantuntijajärjestelmät; diagnoosi; tiedonlouhinta; koneoppiminen; artificial intelligence; expert systems; patient diagnosis; data mining; machine learning

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APA (6th Edition):

Laurikkala, J. (2001). Knowledge Discovery for Female Urinary Incontinence Expert System . (Doctoral Dissertation). Tampere University. Retrieved from http://tampub.uta.fi/handle/10024/67407

Chicago Manual of Style (16th Edition):

Laurikkala, Jorma. “Knowledge Discovery for Female Urinary Incontinence Expert System .” 2001. Doctoral Dissertation, Tampere University. Accessed July 15, 2019. http://tampub.uta.fi/handle/10024/67407.

MLA Handbook (7th Edition):

Laurikkala, Jorma. “Knowledge Discovery for Female Urinary Incontinence Expert System .” 2001. Web. 15 Jul 2019.

Vancouver:

Laurikkala J. Knowledge Discovery for Female Urinary Incontinence Expert System . [Internet] [Doctoral dissertation]. Tampere University; 2001. [cited 2019 Jul 15]. Available from: http://tampub.uta.fi/handle/10024/67407.

Council of Science Editors:

Laurikkala J. Knowledge Discovery for Female Urinary Incontinence Expert System . [Doctoral Dissertation]. Tampere University; 2001. Available from: http://tampub.uta.fi/handle/10024/67407


Tampere University

14. Viikki, Kati. Machine Learning on Otoneurological Data: Decision Trees for Vertigo Diseases .

Degree: fi=Tietojenkäsittelytieteiden laitos | en=Department of Computer Sciences|, 2002, Tampere University

 Asiantuntijajärjestelmät ovat tietokoneohjelmia, jotka ratkaisevat asiantuntijatason tietämystä vaativia reaalimaailman ongelmia. Niiden suorituskyky perustuu sovellusalueen tietämykseen, joka on upotettu tietämyskantaan, sekä tietämystä hyödyntäviin päättelymenetelmiin. Tarkastelen väitöskirjatyössäni… (more)

Subjects/Keywords: koneoppiminen; päätöspuut; datan esikäsittely; asiantuntijajärjestelmät; huimaus; machine learning; decision tree induction; data pre-processing; expert systems; vertigo

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APA (6th Edition):

Viikki, K. (2002). Machine Learning on Otoneurological Data: Decision Trees for Vertigo Diseases . (Doctoral Dissertation). Tampere University. Retrieved from http://tampub.uta.fi/handle/10024/67211

Chicago Manual of Style (16th Edition):

Viikki, Kati. “Machine Learning on Otoneurological Data: Decision Trees for Vertigo Diseases .” 2002. Doctoral Dissertation, Tampere University. Accessed July 15, 2019. http://tampub.uta.fi/handle/10024/67211.

MLA Handbook (7th Edition):

Viikki, Kati. “Machine Learning on Otoneurological Data: Decision Trees for Vertigo Diseases .” 2002. Web. 15 Jul 2019.

Vancouver:

Viikki K. Machine Learning on Otoneurological Data: Decision Trees for Vertigo Diseases . [Internet] [Doctoral dissertation]. Tampere University; 2002. [cited 2019 Jul 15]. Available from: http://tampub.uta.fi/handle/10024/67211.

Council of Science Editors:

Viikki K. Machine Learning on Otoneurological Data: Decision Trees for Vertigo Diseases . [Doctoral Dissertation]. Tampere University; 2002. Available from: http://tampub.uta.fi/handle/10024/67211

.