Articles by "Education"

 

 

I.  The basic elements of the HMM model (Hidden Markov Model)

Diagram showing basic elements of the HMM model


1. Five basic elements of HMM

In HMM, there are 5 basic elements: {I, O, A, B, π}. Let us have an introduction to these 5 basic elements in combination with the sequence marking task:

 

(1) I: state sequence. Here, it refers to the label behind each word.

(2) O: Observation sequence. Here, it refers to each word itself.

(3) A: State transition probability matrix. Here, it refers to the probability that a certain annotation will transfer to the next annotation.

(4) B: Observation probability matrix, that is, emission probability matrix. Here, it refers to the probability of generating a certain word under a certain label.

(5) π: Initial probability matrix. Here, refers to the initialization probability of each annotation.

in:

I = ( i 1 , i 2 , i 3 . . . i N ) I = ({i_1,i_2,i_3...i_N})I=(i

1

 ,i

2

 ,i

3

 ...i

N

 ) state sequence

O = ( o 1 , o 2 , o 3 . . . o N ) O = ({o_1,o_2,o_3...o_N})O=(o

1

 ,o

2

 ,o

3

 ...o

N

 ) observation sequence

A = [ a i j ] N N A = [a_{ij}]_{N*N}A=[a

ij

 ]

NN

  State transition probability matrix

B = [ b j ( k ) ] N N B = [b_j(k)]_{N*N}B=[b

j

 (k)]

NN

  Observation probability matrix

Ï€ initial state probability vector

 

2. HMM model

 

Model: λ = (A,B,π)

A, B, π are the three elements of the Hidden Markov Model

These three elements are obtained through statistics, these three values ​​are the parameters of the model, and the process of obtaining these three values ​​is the process of model training, so the HMM algorithm is a relatively simple algorithm. 

The model has been known, and it can be considered that the fully connected path of each state has been known. Given an observation sequence, the optimal path among all paths is solved by the veterbi algorithm.

 

3. Two assumptions of the HMM model

(1) Homogeneous Markov assumption (also called first-order Markov assumption)

The state of the hidden Markov chain at any time t only depends on the state at the previous time, and has nothing to do with the state at other times and the observed state.

P ( i t i t − 1 , o t − 1 , . . . , i 1 ) = P ( i t i t − 1 ) P(i_t|i_{t-1},o_{t-1},..., i_1) = P(i_t|i_{t-1})P(i

t

 i

t−1

 ,o

t−1

 ,...,i

1

 )=P(i

t

 i

t−1

 )

(2) Observational Independence Hypothesis

The observed state at any time only depends on the state of the Markov chain at that time, and has nothing to do with the observed states at other times.

The above elements can be counted from the training corpus. Finally, based on these statistics, we apply the Viterbi algorithm to calculate the label sequence behind the word sequence.

Diagram showing Hidden Markov Model

 

2. There are three application scenarios for the Hidden Markov Model

We only use one of them for named entity recognition - to find the most likely label sequence behind the observation sequence.

Finally, let's talk about the three problems that HMM solves:

 

1. Evaluation (probability calculation problem)

Knowing the model parameters λ= (A, B, π), calculate the probability of a certain observation sequence, that is, find P(O|λ)

 

2. Learning (learning problems)

Given a sequence of observations O = ( o 1 , o 2 , . . . , o n ) O=(o_1,o_2,...,o_n)O=(o

1

 ,o

2

 ,...,o

n

 ), how to adjust the model parameters λ=(Ï€, A, B) to maximize P(O|λ)? , this is the algorithm to find the model

 

3. Decoding (prediction problem or decoding problem) is the most commonly used

Given an observation sequence O and a model λ, find the most probable state sequence S(s1,s2,…st+1).

For example: through entity labeling and training, we can get the model λ, now given an observation sequence = I work in Phoenix Finance, to find the most likely named entities, and want to find the corresponding state sequence S = (I, in , Phoenix Finance, Work).

 

3. Find the model λ: solve the second problem

HMM is a generative model, so the joint probability is sought

 

Note: When we usually say, finding the model refers to finding the objective function. For example, in linear regression, our objective function is $h(λ)=λ_1X+λ_2$, and finding the objective function only requires the parameter λ, so usually We say that seeking model is seeking parameters.

1

P ( X , Y ) P(X,Y)P(X,Y)

 

Fourth, Viterbi algorithm (Viterbi): solve the third problem

The Viterbi algorithm mainly uses dynamic programming to solve the prediction problem of HMM: always model and observation sequence, find the most probable state sequence

 

Suppose the sequence of states is: x 1 , x 2 , x 3 . . . x N x_1,x_2,x_3 ... x_Nx

1

 ,x

2

 ,x

3

 ...x

N

 

The corresponding observation sequence is: y 1 , y 3 , y 3 . . . y N y_1,y_3,y_3 ... y_Ny

1

 ,y

3

 ,y

3

 ...y

N

 

Then our problem is transformed into: the known input sequence y 1 , y 3 , y 3 . . . y N y_1,y_3,y_3 ... y_Ny

1

 ,y

3

 ,y

3

 ...y

N

 , corresponding to the most probable Chinese characters x 1 , x 2 , x 3 . . . x N x_1,x_2,x_3 ... x_Nx

1

 ,x

2

 ,x

3

 ...x

N

 . What is the most likely sequence of Chinese characters?

formula:

x 1 , x 2 , x 3 . . . x N = A r g M a x P ( x 1 , x 2 , x 3 . . . x N y 1 , y 3 , y 3 . . . y N ) = A r g M a x ∏ i = 1 N P ( y i x i ) P ( x i x i − 1 ) x_1,x_2,x_3 ... x_N = ArgMaxP(x_1,x_2,x_3 ... x_N|y_1,y_3,y_3 . .. y_N) = ArgMax \prod_{i=1}^N P(y_i|x_i)*P(x_i|x_{i-1})x

1

 ,x

2

 ,x

3

 ...x

N

 =ArgMaxP(x

1

 ,x

2

 ,x

3

 ...x

N

 y

1

 ,y

3

 ,y

3

 ...y

N

 )=ArgMax∏

i=1

N

 P(y

i

 x

i

 )P(x

i

 x

i−1

 )

 

where the formula A r g M a x ∏ i = 1 N P ( y i x i ) P ( x i x i − 1 ) ArgMax \prod_{i=1}^N P(y_i|x_i)*P(x_i|x_{i-1 })ArgMax∏

i=1

N

 P(y

i

 x

i

 )P(x

i

 x

i−1

 ) is mainly transformed by the Bayesian formula

 

We know that the Bayesian formula is: P ( A B ) = P ( B A ) P ( A ) P ( B ) P(A|B) = \frac {P(B|A)*P(A )}{P(B)}P(AB)=

P(B)

P(BA)P(A)

 

Then P ( x y ) = P ( y x ) P ( x ) P ( y ) P(x|y) = \frac {P(y|x)*P(x)}{P(y) }P(xy)=

P(y)

P(yx)P(x)

 , where P(y) is a known constant, where P(x) is actually P ( x t x t − 1 ) P(x_t|x_{t-1})P(x

t

 x

t−1

 ), according to the Markov hypothesis, the current moment hypothesis is related to the previous moment.

 

For example, enter the observation sequence:

I love China

O O O B

O B O I

O O B I

B O I B

That is, the third line sought is the optimal path:

 

Fourth, the Viterbi algorithm (Viterbi)

Note: During the calculation of the viberbi algorithm, the shortest path between two points is calculated, not the shortest path between the two layers.

1

1. Nature

If the path p with the highest probability (or the shortest path) passes through a certain point, such as a on the way, then the sub-path Q from the starting point S to a on this path must be the shortest path between S and X22.

Otherwise, replacing Q with the shortest path R from S to a constitutes a shorter path than P, which is obviously contradictory. It is proved that the optimality principle is satisfied.

 

2. Algorithms

If you find the shortest path between S and E, what better way than to traverse all the paths?

 

In fact there must be a shortest path among all paths:

 

 

Let's start solving step by step from scratch:

(1) First, the starting point is S, and there are three possible paths from S to A column: S-A1, S-A2, S-A3, as shown in the following figure:

 

 

We cannot arbitrarily say which segment of S-A1, S-A2, and S-A3 must be part of the global shortest path. So far, any segment may be an alternative to the global shortest path.

(2). Then start the second layer

<1> We start with the first node of the second layer:

 

As shown above, there are only 3 paths through B1:

 

S-A1-B1

 

S-A2-B1

 

S-A3-B1

If the final second layer node passes through B1, the shortest path must be selected from these three paths, then the other two can be deleted.

<2> Then we start the second node of the second layer:

 

 

Similarly, as shown in the figure above, there are 3 paths through B2:

 

S-A1-B2

 

S-A2-B2

 

S-A3-B2

If the final second layer node passes through B2, the shortest path must be selected from these three paths, and the other two can be deleted.

 

<3> Then we start the third node of the second layer:

 

Similarly, as shown in the figure above, there are also 3 paths through B3:

 

S-A1-B3

 

S-A2-B3

 

S-A3-B3

If the final second layer node passes through B3, one of the shortest paths must be selected from these three paths, and the other two can be deleted.

<4> After all the stages of the second layer are traversed, there are three paths left.

 

We don't yet have enough information to tell which one must be a subpath of the global shortest path.

(3) Then we continue the algorithm at the third layer:

 

We don't yet have enough information to tell which one must be a subpath of the global shortest path.

(4) Then we continue the algorithm at the last layer:

 

Point E is already the end point. We can know which one is the shortest path by comparing the total length of the three paths.

 

 

Tips explaining How to Write a Business Blog

A business blog has a lot more to them than just selling the service or the product by way of search engine optimization hence we need t o know how to write a business blog. While writing a business blog, you must make sure that you give away a lot of information to the potential clients who might have logged on to your website solely for the purpose. If the information shared has an essence of the company’s motto, it will certainly reflect in the increase of sale eventually. Listed underneath are some of the important pointers that you should remember while writing the business blog.

How to write a Business Blog in a good manner, thumb shown in green for good writing.

Research the readers in order to know how to write a Business Blog

when you make a product or sell a service, you tend to research a lot for the probability of running the business successfully. 
Similarly, when you know the audience that you are targeting, you will eventually get the material that you should be talking about on your blog. 
Understanding the demographics of the prospective audience will help you in getting the most out of your blog. So get to know the age of the readers, education level, general interest and the source of their information gathering.
How to Write a Business Blog? Text

Make a rough draft

  before posting the article on to the blog, you must make a rough draft on the point you want to cover and ensure that you have an outline ready. It will help you in clarifying all the points, and you will not miss out on any critical detail on the particular topic. Planning the content strategy is essential. 
You must schedule a month and note down the timeline of posting on the topics. In case there are any special days wherein your sales are bound to increase, posting around that time will help you in creating a good reminder in the minds of the prospective clients.

Keep the looks in mind

 business blogs should mean business only to an extent. There are other aspects that you need to consider. The length of each paragraph, inclusions of graphics and images to support your content and how can you make it more interesting will help you in piquing the interest of the reader. If they find the content compelling enough, they will come back wanting for more. The layout of the blog also matters a lot. Use colors and background images that are in synchronization with the company policy and make sure that the font used is legible for the readers.

Editing the article

 when you write the article yourself, you can be blindsided by the mistakes that you make. If you get someone else to read the article, they can provide a different perspective on the content and will give their unbiased opinion. However, make sure that the person editing your article is deft in the language and knows your business well. You could hire someone for professional services or use the online tools that are available for such purposes online. They are quick in determining the tone of the article and can make suggestions for the grammatical corrections that you can make in the article. 


Tags: #Business blog #Blog #Blogging #Writing a Business Blog

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Since the businesses came online there is a lot of competition an d confusion as to which portal should sellers use for selling. Which portal will be in the biggest advantage to the sellers in all the respects. Luckonluck is a web portal which is decentralized with not forcing its own conditions on the sellers. Sellers are fully free and can promote their own name and have their own prominence in the market. While other portals try to dominate sellers, luckonluck only helps them as and however they want. It may only provide suggestions but not force or even use its own courier services to have its own ownership in the entire business.  This social entrepreneurship by them is in the best form as it facilitates the society with honest and fair opportunities and does not eat away their own income by bringing in technology in between.

Social entrepreneurship luckonluck


Sellers aligned with luckonluck only get business form it and may use the portal for business procurement and management and get many benefits that the portal offer. The portal also negotiates deals with companies for its sellers. Thus sellers in advantage in all the respects.


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Taxi Showing Seropt Sign
Seropt Taxi Booking Service App

The entrepreneur has to agree to take bookings from Seropt app only and no other app. Entrepreneurs may take direct booking as well from customers but without any app or service, except Seropt.

Business Plan of Seropt Cab Booking App

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Showing Taxi Top with Seropt.in sign for Taxi or Cab booking through phone or computer
Seropt Cab Booking App
Seropt's commission on the drives shall be 14% only of the fare that too after taxes. Seropt shall not seek any share from the vehicle owner unless they have attained business of at least INR 2, 00, 000 of bill value i.e. including taxes through Seropt regardless of the term when they achieve this much of business.
Usage of any other app part from that of Seropt during the contract period shall be treated as fraud and a crime u/s 420 and 120B of the IPC.
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Showing Trucks carrying load from place to place. Simulating Truck Booking via Serpot App.
Freight Booking
Seropt also provides freight booking though Trucks within India.
 
Seropt app look simulation for an idea how cab booking through mobile or computer looks.
Seropt Car Booking App Simulation
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