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#MLBook #machine-learning #weight

Some algorithms, like SVM, allow the data analyst to provide weightings for each class. These weightings influence how the decision boundary is drawn. If the weight of some class is high, the learning algorithm tries to not make errors in predicting training examples of this class (typically, for the cost of making an error elsewhere).

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Flashcard 4968281672972

Tags

#MLBook #clustering #dimensionality-reduction #machine-learning #model #outlier-detection #unsupervised-learning

Question

In unsupervised learning, the dataset is [...]. Again, \(\mathbf x\) is a feature vector, and the goal of an unsupervised learning algorithm is to create a model that takes a feature vector \(\mathbf x\) as input and either transforms it into another vector or into a value that can be used to solve a practical problem. For example, in clustering , the model returns the id of the cluster for each feature vector in the dataset. In dimensionality reduction, the output of the model is a feature vector that has fewer features than the input \(\mathbf x\); in outlier detection, the output is a real number that indicates how \(\mathbf x\) is different from a “typical” example in the dataset.

Answer

a collection of unlabeled examples \(\{\mathbf x_i\}^N_{i=1}\)

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In unsupervised learning, the dataset is a collection of unlabeled examples \(\{\mathbf x_i\}^N_{i=1}\). Again, \(\mathbf x\) is a feature vector, and the goal of an unsupervised learning algorithm is to create a model that takes a feature vector \(\mathbf x\) as input and either transfor

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Flashcard 5525591690508

Tags

#continuum-mechanics #has-images

Question

Describe some simplifications which arise when considering a single fixed reference configuration.

Answer

When working with a single fixed reference configuration, as we will most often do, one can dispense with talking about the body \(\mathcal B\), a configuration \(\chi\) and the particle \(p\), and work directly with the region \(\mathcal R_\textrm{ref}\), the deformation \(\mathbf y \left( \mathbf x \right)\) and the position \( \mathbf x\).

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Flashcard 6178543635724

Tags

#cinemática #has-images #mecanismos

Question

Defina cinemática.

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Flashcard 6178550451468

Tags

#has-images #mecanismos #partícula

Question

Defina partícula.

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Flashcard 6178557267212

Tags

#corpo #corpo-deformável #corpo-rígido #has-images #mecanismos

Question

Defina corpo, corpo rígido e corpo deformável.

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Flashcard 6180536454412

Tags

#has-images #mecanismos

Question

Defina acoplamento, acoplamento direto e acoplamento indireto.

Answer

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Flashcard 6180568173836

Tags

#atuador #has-images #mecanismos

Question

Defina atuador.

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Flashcard 6180571581708

Tags

#has-images #mecanismos #ponto-de-interesse

Question

Discorra sobre ponto de interesse.

Answer

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Flashcard 6180582329612

Tags

#has-images #mecanismos

Question

Defina mecanismo articulado.

Answer

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Flashcard 6180587310348

Tags

#has-images #mecanismos #representações-de-mecanismos

Question

De que formas um mecanismo pode ser representado?

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Flashcard 6180602514700

Tags

#estrutura #has-images #mecanismos

Question

Defina o significado de estrutura de um mecanismo ou cadeia cinemática.

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Flashcard 6180605922572

Tags

#grau-de-liberdade #has-images #mecanismos

Question

Defina grau de liberdade.

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Flashcard 6180609330444

Tags

#espaço-de-trabalho #espaço-de-trabalho-espacial #espaço-de-trabalho-planar #has-images #mecanismos

Question

Discorra sobre espaço de trabalho, dimensão do espaço de trabalho (\(\lambda\)), espaço de trabalho planar e espaço de trabalho espacial.

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Flashcard 6180612738316

Tags

#circuito #has-images #mecanismos

Question

Discorra sobre circuito e número de circuitos independentes (\(\nu\)).

Answer

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Flashcard 6180620078348

Tags

#análise #has-images #mecanismos #síntese

Question

Conceitue e diferencie análise e síntese de mecanismos.

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Flashcard 6180635806988

Tags

#deslocamento #has-images #mecanismos

Question

Discorra sobre deslocamento.

Answer

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Flashcard 6194683841804

Tags

#espaço-amostral #evento

Question

Defina espaço amostral e evento.

Answer

Em teoria das probabilidades, o espaço amostral ou espaço amostral universal, geralmente denotado S, E, Ω ou U (de "universo"), de um experimento aleatório é o conjunto de todos os resultados possíveis do experimento. Por exemplo, se o experimento é lançar uma moeda e verificar a face voltada para cima, o espaço amostral é o conjunto \({\displaystyle \{cara,coroa\}}\). Para o lançamento de um dado de seis faces, o espaço amostral é \({\displaystyle \{1,2,3,4,5,6\}}\). Qualquer subconjunto de um espaço amostral é comumente chamado um evento, enquanto subconjuntos de um espaço amostral contendo apenas um único elemento são chamados de eventos elementares ou eventos atômicos.

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Espaço amostral – Wikipédia, a enciclopédia livre
abilidade condicional Independência Independência condicional Lei da probabilidade total Lei dos grandes números Teorema de Bayes Desigualdade de Boole Diagrama de Venn Diagrama de árvore v d e <span>Em teoria das probabilidades, o espaço amostral ou espaço amostral universal, geralmente denotado S, E, Ω ou U (de "universo"), de um experimento aleatório é o conjunto de todos os resultados possíveis do experimento. Por exemplo, se o experimento é lançar uma moeda e verificar a face voltada para cima, o espaço amostral é o conjunto { c a r a , c o r o a } {\displaystyle \{cara,coroa\}} . Para o lançamento de um dado de seis faces, o espaço amostral é { 1 , 2 , 3 , 4 , 5 , 6 } {\displaystyle \{1,2,3,4,5,6\}} . Qualquer subconjunto de um espaço amostral é comumente chamado um evento, enquanto subconjuntos de um espaço amostral contendo apenas um único elemento são chamados de eventos elementares ou eventos atômicos. Para alguns tipos de experimentos, podem existir dois ou mais espaços amostrais possíveis plausíveis. Por exemplo, quando retirado uma carta de um baralho de 52 cartas, uma possibilidad

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